Olivier Lartillot

• Jesper Andersen, Olivier Lartillot & Stefania Serafin (2026). Exploring Real-Time Visualizations as Support for Concert experiences for Hearing-Impaired. I Sara Comai & Aikaterini Tzompanaki (Red.), GoodIT '26: Proceedings of the 2026 International Conference on Information Technology for Social. Association for Computing Machinery (ACM). ISSN 9798400724831. s. 550–560. doi: 10.1145/3794786.3830797. Fulltekst i vitenarkiv Vis sammendrag

  This study evaluates two visualization strategies designed to enhance concert experience for individuals with hearing impairments. One visualization was integrated into a performance by a classical string quartet, while the other accompanied a band performance. A total of 67 participants, including 39 individuals with hearing impairments, evaluated their experience through questionnaires. Additionally, in-depth interviews were conducted with 16 participants from the hearing-impaired group. Analysis of the questionnaire data revealed no significant differences in visualization preferences between hearing impaired and normal hearing participants, though individual preferences varied considerably. The interview data offered valuable insights into participants’ subjective experiences. Several participants reported that the visualizations were cognitively taxing and, at times, distracting, pulling their focus away from the music rather than enhancing it. Many expressed a preference for clearer introductions to the visualization strategies before the concert, indicating that a better preparation for the audience could help alleviate cognitive load. Overall, the findings suggest that, while real-time visualizations hold promise as an accessibility tool, their current implementation may not sufficiently enhance the concert experience for hearing-impaired audiences. Future research should focus on simplifying the visual design and incorporating pre-concert orientation. These results contribute to the growing body of knowledge on inclusive practices in the performing arts and highlight important avenues for continued research and development.

• Anna-Maria Christodoulou & Olivier Lartillot (2025). A Multimodal Dataset of Greek Folk Music. I Elsa De Luca (Red.), DLfM '25: Proceedings of the 12th International Conference on Digital Libraries for Musicology. Association for Computing Machinery (ACM). ISSN 9798400720833. s. 19–27. doi: https:/dl.acm.org/doi/10.1145/3748336.3748339. Fulltekst i vitenarkiv Vis sammendrag

  This paper presents a multimodal dataset of Greek folk dance music, focusing on syrtos and balos. Developed to support research in computational musicology, the dataset improves access to Greek musical heritage through manually transcribed MIDI scores, aligned lyrics, and rich metadata, all curated by expert musicologists. Through pattern analysis and feature extraction, we examine both shared melodic structures and unique characteristics of each dance, with some examples reflecting traces of oral transmission. While metadata accompanies the collection to support organization and context, our primary emphasis is on the musical and lyrical content. This work contributes to digital ethnomusicology by showing how multimodal datasets of folk music can inform both analytical research and cultural heritage preservation.

• Anna-Maria Christodoulou, Kyrre Glette, Olivier Lartillot & Alexander Refsum Jensenius (2025). MusiQAl: A Dataset for Music Question–Answering through Audio–Video Fusion. Transactions of the International Society for Music Information Retrieval. 8(1), s. 265–282. doi: 10.5334/tismir.222. Fulltekst i vitenarkiv Vis sammendrag

  Music question–answering (MQA) is a machine learning task where a computational system analyzes and answers questions about music‑related data. Traditional methods prioritize audio, overlooking visual and embodied aspects crucial to music performance understanding. We introduce MusiQAl, a multimodal dataset of 310 music performance videos and 11,793 human‑annotated question–answer pairs, spanning diverse musical traditions and styles. Grounded in musicology and music psychology, MusiQAl emphasizes multimodal reasoning, causal inference, and cross‑cultural understanding of performer–music interaction. We benchmark AVST and LAVISH architectures on MusiQAI, revealing strengths and limitations, underscoring the importance of integrating multimodal learning and domain expertise to advance MQA and music information retrieval.

• Olivier Lartillot, Dana Swarbrick, Finn Upham & Carlos Eduardo Cancino-Chacón (2025). Video Visualization of a String Quartet Performance of a Bach Fugue: Design and Subjective Evaluation. Music & Science. 8. doi: 10.1177/20592043251352299. Fulltekst i vitenarkiv Vis sammendrag

  Visualizing music—through music notation, analytical representations, or music videos—might potentially boost the appreciation of music in all its richness. The purpose of this study was to design and test a visualization strategy aimed at explicating to a large audience with diverse backgrounds—especially novices—the multifaceted beauty of the final Contrapunctus in J.S. Bach's The Art of Fugue, performed by the Danish String Quartet. At the surface level of the musical structure, the rich fluctuation of pitch shaped by each musician was depicted in the form of undulating pitch curves. At a deeper structural level, the repetition of pitch curves, distinctive of fugues, was highlighted through vertical alignment—inspired by a technique called paradigmatic analysis, originating from anthropology and music semiology. The visualization was initially prototyped in the form of a real-time technology as part of the MusicLab Copenhagen research concert. The concert audience focused on the performance itself, and did not pay much attention to, nor appreciate, the visualization. To evaluate more thoroughly the potential of the visualization, participants with varied musical expertise and taste were invited to listen to a recorded performance of the piece and watch the visualization on their own computer. A large majority reported that they felt they understood the visualization, around half of them felt that it enhanced their musical understanding, and a small group felt that it helped them to better appreciate the music.

• Simon Høffding, Rebecca Josefine Five Bergstrøm, Laura Bishop, Pedro Pablo Lucas Bravo, Kayla Burnim & Carlos Eduardo Cancino-Chacón [Vis alle 28 forfattere av denne artikkelen] (2025). Introducing the MusicLab Copenhagen Dataset. Music & Science. 8. doi: 10.1177/20592043241303288. Fulltekst i vitenarkiv Vis sammendrag

  MusicLab Copenhagen was a unique research concert featuring the world-renowned Danish String Quartet in a naturalistic setting. The audience was split between one group physically located in the hall, another group listening to a radio broadcast, and a third group watching a live stream. Qualitative and quantitative data were captured from both musicians and audiences, resulting in a comprehensive dataset that can be used to address many research questions. This document introduces the dataset, explains its structure, and reflects on the related data collection, storing, publishing, and archiving processes.

• Anna-Maria Christodoulou, Sagar Dutta, Olivier Serge Gabriel Lartillot, Kyrre Glette & Alexander Refsum Jensenius (2024). Exploring Convolutional Neural Network Models for Multimodal Classification of Expressive Piano Performance, Proceedings of the Sound and Music Computing Conference 2024. SMC Network. ISSN 9789893520758. Fulltekst i vitenarkiv Vis sammendrag

  This paper addresses improving performance analysis by automating the recognition of expressive performance styles. We propose a multimodal fusion approach integrating audio, video, and motion data. We demonstrate the effectiveness of our approach by utilizing convolutional neural network (CNN) models. Training is done on a classical piano dataset of 211 excerpts containing audio, video, MIDI, and motion capture data. The results highlight the robustness of the CNN models; they achieve high accuracy even when trained on a limited dataset. Our study contributes to advancing the field of performance analysis by applying deep learning techniques to multimodal data.

• Anne Danielsen, Ragnhild Brøvig, Kjetil Klette Bøhler, Guilherme Schmidt Câmara, Mari Romarheim Haugen & Eirik Jacobsen [Vis alle 13 forfattere av denne artikkelen] (2024). There’s More to Timing than Time: Investigating Musical Microrhythm Across Disciplines and Cultures. Music Perception. ISSN 0730-7829. 41(3), s. 176–198. doi: 10.1525/mp.2024.41.3.176. Fulltekst i vitenarkiv Vis sammendrag

  The TIME project: Timing and Sound in Musical Microrhythm (2017–2022) studied microrhythm; that is, how dynamic envelope, timbre, and center frequency, as well as the microtiming of a variety of sounds, affect their perceived rhythmic properties. The project involved theoretical work regarding the basic aspects of microrhythm; experimental studies of microrhythm perception, exploring both stimulus features and the participants’ enculturated expertise; observational studies of how musicians produce particular microrhythms; and ethnographic studies of musicians’ descriptions of microrhythm. Collectively, we show that: (a) altering the microstructure of a sound (“what” the sound is) changes its perceived temporal location (“when” it occurs), (b) there are systematic effects of core acoustic factors (duration, attack) on microrhythmic perception, (c) microrhythmic features in longer and more complex sounds can give rise to different perceptions of the same sound, and (d) musicians are highly aware of microrhythms and have developed vocabularies for describing them. In addition, our results shed light on conflicting results regarding the effect of microtiming on the “grooviness” of a rhythm. Our use of multiple, interdisciplinary methodologies enabled us to uncover the complexity of microrhythm perception and production in both laboratory and real-world musical contexts.

• Olivier Lartillot (2024). Musicological and Technological Perspectives on Computational Analysis of Electroacoustic Music. I Alexander Refsum Jensenius (Red.), Sonic Design: Explorations Between Art and Science. Springer Nature. ISSN 9783031578922. s. 271–297. doi: 10.1007/978-3-031-57892-2_15. Fulltekst i vitenarkiv Vis sammendrag

  Analysing electroacoustic music remains challenging, leaving this artistic treasure somewhat out of reach of mainstream musicology and many music lovers. This chapter examines electroacoustic music analysis, covering musicological investigations and desires and technological challenges and potentials. The aim is to develop new technologies to overcome the current limitations. The compositional and musicological foundations of electroacoustic music analysis are based on Pierre Schaeffer’s Traité des objects musicaux. The chapter presents an overview of core analytical principles underpinning more recent musicological approaches, including R. Murray Schafer’s soundscape analysis, Denis Smalley’s spectro-morphology, and Lasse Thoresen’s graphical formalisation. Then the state of the art in computational analysis of electroacoustic music is compiled and organised along broad themes, from detecting sound objects to estimating dynamics, facture and grain, mass, motions, space, timbre and rhythm. Finally, I sketch the principles of what could be a Toolbox des objets sonores.

• Anna-Maria Christodoulou, Olivier Lartillot & Alexander Refsum Jensenius (2024). Multimodal music datasets? Challenges and future goals in music processing. International Journal of Multimedia Information Retrieval. ISSN 2192-6611. 13(3). doi: 10.1007/s13735-024-00344-6. Fulltekst i vitenarkiv Vis sammendrag

  The term “multimodal music dataset” is often used to describe music-related datasets that represent music as a multimedia art form and multimodal experience. However, the term “multimodality” is often used differently in disciplines such as musicology, music psychology, and music technology. This paper proposes a definition of multimodality that works across different music disciplines. Many challenges are related to constructing, evaluating, and using multimodal music datasets. We provide a task-based categorization of multimodal datasets and suggest guidelines for their development. Diverse data pre-processing methods are illuminated, highlighting their contributions to transparent and reproducible music analysis. Additionally, evaluation metrics, methods, and benchmarks tailored for multimodal music processing tasks are scrutinized, empowering researchers to make informed decisions and facilitating cross-study comparisons.

• Emilio Ros-Fábregas, Kevin Page, Mark Saccomano, Olivier Lartillot & Anastasiia Mazurenko (2024). PERSPECTIVES ON DIGITAL MUSIC EDITION, HERITAGE PRESERVATION, ANNOTATION AND ANALYSIS: A COST ACTION MEETING IN BARCELONA. Anuario Musical. ISSN 0211-3538. doi: 10.3989/anuariomusical.2024.79.560. Fulltekst i vitenarkiv
• Hans-Hinrich Thedens & Olivier Lartillot (2023). AudioSegmentor: Et verktøy for formidling av arkivopptak på nettet. Studia Musicologica Norvegica. ISSN 0332-5024. 49(1), s. 92–101. doi: 10.18261/smn.49.1.7. Fulltekst i vitenarkiv Vis sammendrag

  Norske folkemusikkarkiver står foran utfordringer vedrørende å gjøre sine samlinger tilgjengelige på nett når feltopptak faller i det fri etter 50 år. Denne artikkelen beskriver et forsøk på å tilrettelegge brukskopier i form av lydfiler slik at brukerne kan lytte til enkeltopptak i en nettpresentasjon av et arkivs innhold. Lydklassifiseringsteknologi er i stand til å finne og markere starttidspunkt på enkeltmelodier og spare arkivpersonalet for mange timers manuelt arbeid. Mirage-prosjektet på UiOs RITMO-senter har utviklet et grensesnitt for et slikt verktøy for Nasjonalbibliotekets folkemusikkarkiv og dets nettkatalog WebbFIOL. Løsningen vil kunne tas i bruk av alle som står overfor liknende utfordringer.

• Laura Bishop, Simon Høffding, Olivier Serge Gabriel Lartillot & Bruno Laeng (2023). Mental Effort and Expressive Interaction in Expert and Student String Quartet Performance. Music & Science. 6. doi: 10.1177/20592043231208000. Fulltekst i vitenarkiv
• Olivier Lartillot, Mats Sigvard Johansson, Anders Elowsson, Lars Løberg Monstad & Mattias Storås Cyvin (2023). A Dataset of Norwegian Hardanger Fiddle Recordings with Precise Annotation of Note and Beat Onsets. Transactions of the International Society for Music Information Retrieval. 6(1), s. 186–202. doi: 10.5334/TISMIR.139. Fulltekst i vitenarkiv
• Clemens Maidhof, Viktor Müller, Olivier Lartillot, Kat Agres, Jodie Bloska & Rie Asano [Vis alle 8 forfattere av denne artikkelen] (2023). Intra- and inter-brain coupling and activity dynamics during improvisational music therapy with a person with dementia: an explorative EEG-hyperscanning single case study. Frontiers in Psychology. 14. doi: 10.3389/fpsyg.2023.1155732. Fulltekst i vitenarkiv
• Alexander Szorkovszky, Frank Veenstra, Olivier Serge Gabriel Lartillot, Alexander Refsum Jensenius & Kyrre Glette (2023). Embodied Tempo Tracking with a Virtual Quadruped, Proceedings of the Sound and Music Computing Conference 2023. SMC Network . ISSN 9789152773727. doi: 10.5281/zenodo.10060970. Fulltekst i vitenarkiv Vis sammendrag

  Dynamic attending theory posits that we entrain to time-structured events in a similar way to synchronizing oscillators. Hence, a tempo tracker based on oscillators may replicate humans' ability to rapidly and robustly identify musical tempi. We demonstrate this idea using virtual quadrupeds, whose gaits are controlled by oscillatory neural circuits known as central pattern generators (CPGs). The quadruped CPGs were first optimized for flexible gait frequency and direction, and then an additional recurrent layer was optimized for entrainment to isochronous pulses. Using excerpts of musical pieces, we find that the motion of these agents can rapidly entrain to simple rhythms. Performance was found to be partially predicted by pulse entropy, a measure of the sample's rhythmic complexity. Notably, in addition to having wide tempo ranges, the best performing agents can also entrain to rhythms that are periodic but not quantized on a grid. Our approach offers an embodied alternative to other dynamical systems-based approaches to entrainment, such as gradient-frequency arrays. Such agents could find use as participants in virtual musicking environments, or as real-world musical robots.

• Olivier Lartillot, Anders Elovsson, Mats Sigvard Johansson, Hans-Hinrich Thedens & Lars Alfred Løberg Monstad (2022). Segmentation, Transcription, Analysis and Visualisation of the Norwegian Folk Music Archive. I Laurent Pugin (Red.), DLfM '22: 9th International Conference on Digital Libraries for Musicology. Association for Computing Machinery (ACM). ISSN 9781450396684. s. 1–9. doi: 10.1145/3543882.3543883. Fulltekst i vitenarkiv Vis sammendrag

  We present an ongoing project dedicated to the transmutation of a collection of field recordings of Norwegian folk music established in the 1960s into an easily accessible online catalogue augmented with advanced music technology and computer musicology tools. We focus in particular on a major highlight of this collection: Hardanger fiddle music. The studied corpus was available as a series of 600 tape recordings, each tape containing up to 2 hours of recordings, associated with metadata indicating approximate positions of pieces of music. We first need to retrieve the individual recording associated with each tune, through the combination of an automated pre-segmentation based on sound classification and audio analysis, and a subsequent manual verification and fine-tuning of the temporal positions, using a home-made user interface. Note detection is carried out by a deep learning method. To adapt the model to Hardanger fiddle music, musicians were asked to record themselves and annotate all played note, using a dedicated interface. Data augmentation techniques have been designed to accelerate the process, in particular using alignment of varied performances of same tunes. The transcription also requires the reconstruction of the metrical structure, which is particularly challenging in this style of music. We have also collected ground-truth data, and are conceiving a computational model. The next step consists in carrying out detailed music analysis of the transcriptions, in order to reveal in particular intertextuality within the corpus. A last direction of research is aimed at designing tools to visualise each tune and the whole catalogue, both for musicologists and general public.

• Patrik N. Juslin, Laura S. Sakka, Gonçalo T. Barradas & Olivier Lartillot (2022). Emotions, mechanisms, and individual differences in music listening: A stratified random sampling approach. Music Perception. ISSN 0730-7829. 40(1), s. 55–86. doi: 10.1525/mp.2022.40.1.55. Fulltekst i vitenarkiv Vis sammendrag

  Emotions have been found to play a paramount role in both everyday music experiences and health applications of music, but the applicability of musical emotions depends on: 1) which emotions music can induce, 2) how it induces them, and 3) how indi- vidual differences may be explained. These questions were addressed in a listening test, where 44 participants (aged 19–66 years) reported both felt emotions and subjective impressions of emotion mechanisms (Mec Scale), while listening to 72 pieces of music from 12 genres, selected using a stratified random sampling pro- cedure. The results showed that: 1) positive emotions (e.g., happiness) were more prevalent than negative emotions (e.g., anger); 2) Rhythmic entrainment was the most and Brain stem reflex the least frequent of the mechanisms featured in the BRECVEMA theory; 3) felt emotions could be accurately predicted based on self- reported mechanisms in multiple regression analyses; 4) self-reported mechanisms predicted felt emotions better than did acoustic features; and 5) individual listeners showed partly different emotion-mechanism links across stimuli, which may help to explain individual differences in emotional responses. Implications for future research and applications of musical emotions are discussed.

• Olivier Lartillot, Kristian Nymoen, Guilherme Schmidt Câmara & Anne Danielsen (2021). Computational localization of attack regions through a direct observation of the audio waveform. Journal of the Acoustical Society of America. ISSN 0001-4966. 149(1), s. 723–736. doi: 10.1121/10.0003374. Fulltekst i vitenarkiv Vis sammendrag

  This article addresses the computational estimation of attack regions in audio recordings. Previous attempts to do so were based on the reduction of the audio waveform into an envelope curve, which decreases its temporal resolution. The proposed approach detects the attack region directly from the audio waveform. The attack region is modeled as a line starting from a low-amplitude point and intersecting one of the local maxima according to two principles: (1) maximizing the slope, while favoring, at the same time, a higher peak if the slope remains only slightly lower and (2) dismissing initial attack regions of relatively low amplitude. The attack start position is fine-tuned by intersecting the attack slope with the audio waveform. The proposed method precisely pinpoints the attack region in cases where it is unambiguously observable from the waveform itself. In such cases, previous methods selected a broader attack region due to the loss of temporal resolution. When attack regions are less evident, the proposed method’s estimation remains within the range of results provided by other methods. Applied to the prediction of judgments of P-center localization [Danielsen, Nymoen, Anderson, C^amara, Langerød, Thompson, and London, J. Exp. Psychol. Hum. Percept. Perform. 45, 402–418 (2019)], the proposed method shows a significant increase in precision, at the expense of recall.

• Olivier Lartillot (2021). Computational Musicological Analysis of Notated Music: a Brief Overview. Nota Bene. ISSN 1891-4829. 15, s. 142–161. Fulltekst i vitenarkiv Vis sammendrag

  I present a short overview of computational methods for musicological analysis of notated music. We first need to clarify the various levels of computational representations of music: on one side, notated music, on the other, audio recordings, and in the middle, a note-level representa- tion of music performance where higher-level musical descriptions are absent. The article provides a synthetic and partial panorama of the different types of music analysis that have been systematised and auto- mated using computers. While pioneering works were mainly focused on statistical descriptions of the surface of music, other dimensions of music analysis such as harmony, metre and structure have been taken into consideration since. I conclude by sketching my personal vision of the future of computational music analysis.

• Anders Elovsson & Olivier Lartillot (2021). A Hardanger Fiddle Dataset with Performances Spanning Emotional Expressions and Annotations Aligned using Image Registration, Proceedings of the 22nd International Society for Music Information Retrieval Conference, Online, Nov 7-12, 2021. International Society for Music Information Retrieval. ISSN 9781732729902. s. 174–181. Fulltekst i vitenarkiv Vis sammendrag

  This paper presents a Hardanger fiddle dataset “HF1” with polyphonic performances spanning five different emotional expressions: normal, angry, sad, happy, and tender. The performances thus cover the four quadrants of the activity/valence-space. The onsets and offsets, together with an associated pitch, were human-annotated for each note in each performance by the fiddle players themselves. First, they annotated the normal version. These annotations were then transferred to the expressive performances using music alignment and finally human-verified. Two separate music alignment methods based on image registration were developed for this purpose; a B-spline implementation that produces a continuous temporal transformation curve and a Demons algorithm that produces displacement matrices for time and pitch that also account for local timing variations across the pitch range. Both methods start from an “Onsetgram” of onset salience across pitch and time and perform the alignment task accurately. Various settings of the Demons algorithm were further evaluated in an ablation study. The final dataset is around 43 minutes long and consists of 19 734 notes of Hardanger fiddle music, recorded in stereo. The dataset and source code are available online. The dataset will be used in MIR research for tasks involving polyphonic transcription, score alignment, beat tracking, downbeat tracking, tempo estimation, and classification of emotional expressions.

• Stéphanie Weisser, Olivier Lartillot & Hélène Sechehaye (2021). Investiguer la grésillance. Pour une approche ethno-acoustique du timbre musical. Cahiers d'ethnomusicologie. ISSN 2235-7688. 34, s. 37–58. Fulltekst i vitenarkiv Vis sammendrag

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Se alle arbeider i NVA

• Olivier Lartillot & Anders Elovsson (2025). Annotemus User’s Guide. Universitetet i Oslo. doi: https:/www.uio.no/ritmo/english/projects/mirage/software/annotemus/guide0.7.2.pdf. Fulltekst i vitenarkiv Vis sammendrag

  Annotemus is a new software for computer-assisted annotation of music recordings, with a focus on transcription. Annotemus features a graphical user interface providing functionality for creating annotations on top of a graphical representation of an audio file. Annotation aids include audio playback of the current window, playback between the start and end of one or several selected notes, playback that starts prior to a selected annotated note and ends at the annotated onset position, playback with a click at each annotated onset position, and playback with a synthesized version of the annotated score played in one of the stereo channels. All playback functionality is offered with the option of slowing it down to various speeds.

Se alle arbeider i NVA

• Philippe Vendrix, Rebekah Ahrendt, Judith Irmela Haug & Olivier Lartillot (2026). COST Action EarlyMuse 2022-2026. Six Reports on Musicology in Europe. doi: 10.5281/ZENODO.22337344. Fulltekst i vitenarkiv Vis sammendrag

  This volume gathers the six reports produced between 2025 and 2026 within COST Action CA21161, A New Ecosystem of Early Music Studies (EarlyMuse), the first COST Action ever devoted to historical musicology. Running from September 2022 to October 2026, the Action brought together close to three hundred researchers from some forty countries in five working groups: Education, Sources, Publications, Performances and Policies. Each report was drafted, discussed and published separately, with its own editorial team, corpus, method and primary audience. Assembled here, they describe a single object from six angles: not "early music" as a repertory, but the ecosystem in which musical scholarship is now produced: the departments and conservatoires that train and employ musicologists, the doctoral frameworks that certify their work, the libraries and archives that hold the sources, the digital infrastructures and computational tools that mediate access to them, the European funding architectures that decide which questions become fundable, and the professional and public worlds in which musical knowledge circulates. The six reports cover: musicology as data science and heritage science; the mapping of musicology in European higher education and research institutions; musicology and artificial intelligence; historical musicology and European research infrastructures and initiatives (EOSC, ECCCH/ECHOES, the Data Space for Cultural Heritage, DARIAH, E-RIHS); doctoral-level artistic research in music; and musicology within Horizon Europe co-funded partnerships, notably Resilient Cultural Heritage and the emerging Virtual Worlds partnership. Taken together, they document a growing mismatch between the intellectual vitality of the field and the fragility of the structures through which it is taught, documented, funded and made visible. Recurrent findings include the statistical invisibility of musicology in OECD, Eurostat and UNESCO classifications; the institutional cost of methodological pluralism; and the claim that the field is under-positioned rather than under-qualified for the European agendas now being written. The volume is neither a state of the art nor a synthesis nor a manifesto, but a handover document, a description of the terrain, closing with recommendations addressed to institutions, national funders, European bodies, learned societies and individual researchers.

• Augustin Braud, Alexandra Büttner, Olivier Lartillot, Anna Katharina Laux, Laurent Pugin & Kristina Richts-Matthaei [Vis alle 10 forfattere av denne artikkelen] (2026). Formation of a Special Interest Group on Linked Metadata and Interoperability in Musicology: Report and Results from the International Musicology Workshop Held Between NFDI4Culture, musica*, RISM and the MishMash Centre for AI and Creativity. doi: 10.5281/ZENODO.22868638. Fulltekst i vitenarkiv Vis sammendrag

  The report presents the results and the outcome of an international musicology workshop held at the Academy of Sciences and Literature | Mainz on 7th- 8th May 2026. The workshop brought together for the first time members of musica* – Consortium en musicologie numérique from the French digital humanities infrastructure project Huma-Num and musicologists from NFDI4Culture, the German Consortium for Research Data on Material and Immaterial Cultural Heritage, as well as experts from the RISM Digital Centre of the Répertoire International des Sources Musicales (RISM), based at the Swiss National Library in Basel, and from the MishMash Centre for AI and Creativity, a consortium of Norwegian research institutions focusing on the application of artificial intelligence in the cultural sector. Furthermore, musicologists and developers from the Specialized Information Service (FID) for Musicology (musiconn) and the Centre for Digital Music Documentation (CDMD) were present. At the workshop, the group established a new Special Interest Group on Linked Metadata and Interoperability in Musicology. This group will lay the groundwork for future collaborative efforts with a focus on improving the interoperability and alignment of metadata between the aforementioned projects and consortia. The first steps in this process are documented in this report.

• Lars Løberg Monstad, Olivier Lartillot, Karstein Grønnesby & Martin Fuglseth Kolden (2026). Brukte AI til å transkribere noter for hardingfele – det ga gründerne idéen til ny musikk-app de mener kan bli en «pengemaskin». [Journal]. Shifter. doi: https:/www.shifter.no/nyheter/brukte-ai-til-a-transkribere-noter-for-hardingfele-det-ga-gruenderne-ideen-til-ny-musikk-app-de-mener-kan-bli-en-pengemaskin/460554. Fulltekst i vitenarkiv Vis sammendrag

  Bots For Music vil skape et bibliotek over musikknoter, både for musikk som ikke er laget ennå, eller noter som ikke finnes. Appen skal kunne brukes av profesjonelle musikere, til læring og hobby.

• Olivier Lartillot (2026). Computational (AI) analysis (& curation) of music archives. doi: https:/en.bergenassembly.no/Archival-Intelligence-Epistemic-Shifts-from-Sound-to-Listening. Fulltekst i vitenarkiv
• Joan Gatti, Olivier Lartillot & Lars Løberg Monstad (2026). Hardingfele and AI. doi: https:/mishmash.no/lab/opening-ceremony/#%3A%7E%3Atext=act%20is%20claimed-%2C1%3A03%3A30%2C-Hardingfele%20and%20AI. Fulltekst i vitenarkiv Vis sammendrag

  Skurdalsbruri played by Joan Gatti, with real-time visualisation of the music transcription and melodic analysis

• Olivier Lartillot (2026). Intensive/Extensive Music Analysis and Identification. doi: https:/www.uio.no/ritmo/english/news-and-events/events/conferences/2026/daim/index.html. Fulltekst i vitenarkiv Vis sammendrag

  Olivier Lartillot’s second talk on Day 2 addressed music analysis at two complementary scales: intensive, investigating the internal structure of individual pieces, and extensive, comparing large collections to identify related tunes and characterise musical styles. Sequential patterns provided the connection between these scales, linking detailed motivic analysis to corpus organisation, retrieval, and metadata inference. The talk opened with a visualisation of a Hardanger fiddle performance presented at the MishMash opening event. Conventional notation indicates repeated bars, but the boundariesofmusicalpatternsdonotnecessarilycoincidewithbarlines, andshortermotifs can recur within larger repeated passages. A paradigmatic representation rearranged the score so that corresponding patterns aligned vertically, making these relationships explicit. Applied to Bach’s Contrapunctus XIV from the Art of Fugue, the same approach revealed how the counter-subject draws on recurring fragments whose arrangement varies between appearances. A demonstration from the MusicLab Copenhagen concert with the Danish String Quartet showed how live pitch tracking could animate this analysis during performance, while also illustrating the difficulty of following several simultaneous contrapuntal relationships. Lartillot emphasised that transcription and analysis are closely interconnected. Moving from audio to notation requires note detection, pitch estimation, beat tracking, and rhythmic quantisation, each of which already involves interpreting musical organisation. The opening of Mozart’s G-minor symphony illustrated how repeated accompaniment figures support the inference of beats and metrical groupings. Patterns can themselves consist of smaller patterns, requiring models that accommodate several levels of or- ganisation. Debussy’s Syrinx illustrated exact repetition, altered endings, and rhythmic correspondences despite changes in pitch. It also showed how a new motif can emerge within the ending of a repeated passage. Such relationships suggest that stylistic analysis should consider not only which motifs recur, but also how repetitions are organised and connected. Earlier work with Mondher Ayari on Tunisian istikhbâr improvisation provided a connection to the morning’s discussion of recitation. Here, modal identity depends on characteristic melodicfiguresaswellasscalestructure,makingtherecognitionofmotifsthroughvariation an important analytical task. For Norwegian folk music, Lartillot proposed extending this approach to the construction of phrases and larger forms, the relation between ornamentation and core melodic structure, and the development of an ornamentation taxonomy. Further dimensions included modal organisation, rhythmic figures, expressive timing, and pitch intonation in relation to melodic context. 42 At the corpus scale, the proposed aim was to discover correspondences between pieces without requiring predefined categories, identifying shared patterns, possible quotations, and tune families. These relationships could help characterise genres, dances, musicians, regions, or periods. An interconnected catalogue could offer an overview placing related tunes near one another, links between corresponding passages while browsing individual recordings, and searches formulated around musicological features. Collaboration with Sámi experts at the University of Tromsø raised related possibilities for segmenting yoiks within long recordings, identifying distinct yoiks and performers, and mapping collections throughcontentanalysis. Bothcontinuousaudio-derivedrepresentationsandmorediscrete transcriptions were considered. These proposals formed part of the research agenda for MishMash WP6. Pattern- based comparison could also support metadata inference, using labelled items to suggest attributesforrecordingswhosedocumentationisincomplete. Lartillotenvisagedcombining rule-based modelling with machine learning in a framework of interdependent transcription and analysis processes. Discussion emphasised that the intended users must guide the choice of representations and analytical categories, through close collaboration with experts and musicians. Po- tential uses included research, teaching, and public engagement. A concrete example concerned fiddle tunes in which successive variants retain an ending while changing their beginnings: conventional repeat notation handles this awkwardly, whereas alternative graphical representations could make the shared structure clearer.

• Olivier Lartillot (2026). Computational Transcription of Chant and Yoik. doi: https:/www.uio.no/ritmo/english/news-and-events/events/conferences/2026/daim/index.html. Fulltekst i vitenarkiv Vis sammendrag

  Olivier Lartillot presented preliminary experiments in the computational transcription of chant and yoik. Conceived as a workshop contribution rather than a report on a completed study, the presentation explored how signal-processing methods might support the analysis introduced in the preceding talks, while identifying questions requiring further discussion with musical and cultural experts. Using the same Hungarian lament recording analysed by Biró and van Kranenburg, Lartillot first considered how the beginning and end of individual notes might be detected. A fundamental-frequency (F0) contour provides information about pitch, but does not by itself reveal the precise temporal organisation of a vocal performance. Note boundaries are affected by consonants, vowels, low-energy vocal gestures and changes distributed across the harmonic spectrum. Lartillot therefore proposed tracking the energy of the fundamental and its successive partials. Coordinated increases or decreases across these partials can indicate the onset or offset of a vocal sound, including a brief gesture preceding the principal note. The approach draws on earlier work in which changes in spectral energy were used to detect onsets in complex musical recordings. These experiments used simple signal-processing rules, but Lartillot emphasized that the eventual choice between rule-based and machine-learning methods would depend on the available annotated data. If sufficiently large training collections were not available, signal processing could provide an initial segmentation from which annotations could be corrected and subsequently used to train a model. Such a hybrid workflow would combine interpretable preliminary rules with iterative expert validation. The proposed segmentation also complements the global pitch-histogram method pre- sented by Biró and van Kranenburg. Whereas their approach derives the principal scale positions from peaks in the distribution of F0 values, Lartillot considered first detecting the temporal extent of each note and then estimating its representative frequency locally from the stable portions of its pitch contour. Each estimate could subsequently be related to the scale degrees obtained from the recording as a whole. For long performances in which tuning changes progressively, a single global histogram might be insufficient; the pitch reference would then need to adapt over time. This extension remained to be implemented. Lartillot next presented an exploratory analysis of a yoik recording supplied by Camilla Brattland. Its spectrum showed substantial activity beyond the fundamental, including prominent partial and formant regions suggestive of a diphonic vocal quality. These timbral features would ultimately need to be represented alongside pitch. Even the F0 contour 36 alone revealed rapid movement and extensive oscillation around apparently recurrent pitch levels. Some events appeared comparatively stable, whereas others approached or circled a level without settling on it. The neutral letter labels used in the demonstration were therefore provisional analytical markers rather than Western scale degrees. Slowing down the recording made these details easier to hear and inspect, but Lartillot refrained from proposing a definitive segmentation or categorization before discussing the results with yoik experts. The ensuing discussion broadened the notion of transcription beyond conventional staff notation. Continuous transitions, approaches from above or below, oscillations and the presence or absence of clear note boundaries may carry information that a sequence of discrete pitches cannot preserve. Participants also asked how computational results could be made accessible without relying on visual representation, as well as to tradition bearers who do not use Western notation. Lartillot suggested synchronizing analytical information with the original recording, enabling rapid auditory comparison between passages, and sonifying aspects of the analysis. The presentation thus framed transcription as an iterative, expert-guided process in which computational representations should remain connected to both the sounding material and the ways in which the tradition itself distinguishes musically meaningful features.

• Alexander Refsum Jensenius, Ingrid Romarheim Haugen, Arnulf Christian Mattes & Olivier Lartillot (2026). MishMash Work Package 6: AI for Cultural Heritage. doi: https:/www.uio.no/ritmo/english/news-and-events/events/conferences/2026/daim/index.html. Fulltekst i vitenarkiv Vis sammendrag

  Alexander Refsum Jensenius opened Day 2 by welcoming participants to the University of Oslo and introducing MishMash—the Centre for AI & Creativity, a national consortium led by RITMO and bringing together more than 200 researchers and practitioners. The centre examines the possibilities, limitations, and wider consequences of creative AI through seven thematic work packages. Its activities follow three interconnected approaches: creating new AI systems and practices, exploring their uses in different contexts, and reflecting critically on their cultural and societal implications. Jensenius also outlined MishMash’s role, alongside the National Library of Norway and the Department of Musicology, in shaping the symposium and situating its focus on music and cultural heritage within this broader interdisciplinary programme. Ingrid Romarheim Haugen, Arnulf Mattes, and Olivier Lartillot then introduced Work Package 6: AI for Cultural Heritage. WP6 starts from the central role of archives, libraries, and museums in preserving and communicating human culture, and from the rapidly growing scale of their digital collections. The National Library provides an important foundationthroughitsextensivedigitisationprogrammeandDigitalHumanitiesLaboratory, butmanyresourcesremaindistributedacrossnationalandlocalinstitutions. WP6therefore seeks to improve their discovery, organisation, interpretation, and presentation, while contributing to a more coherent digital framework connected to initiatives such as the European Cultural Heritage Cloud. The work package proposes hybrid AI methods combining machine learning with mu- sicological knowledge and cognition-based symbolic approaches. Its current activities include the transcription and segmentation of audio collections, computational analysis of Norwegian folk music and Hardanger fiddle repertoire, metadata inference, and the development and interlinking of digital work catalogues. These activities also connect with the National Library’s digitisation programme and the proposed MusikkData research infrastructure. Particular attention is given to minority cultural expressions, including Sámi joik and world chant traditions. The speakers also proposed several new topics that could be introduced into ongoing discussions within WP6. These included generating richer metadata from basic music catalogues through computational analysis; linking music transcription and analysis with metadata and machine-readable musicological and editorial discourse through semantic- web technologies; and automatically detecting the reuse of musical material across a repertoire. Grieg’s Peer Gynt, in connection with the National Ibsen Year in 2028, was mentioned as one possible setting in which some of these ideas might be developed. 27 The presentation illustrated the broader scope of WP6 through projects involving different cultural materials and institutions. These included the reconstruction of provenance and circulation networks behind the fragmented Schøyen Collection; semantic image search across the collections of the National Museum and other institutions; the Phygital Lab’s integration of physical and digital museum experiences; and the preservation and dissemination of craft knowledge from the archive of the Women’s School of Applied Arts and Crafts. Together, these examples showed how methods developed for one type of collection may inform work in other areas of cultural heritage. Finally, the speakers stressed that technological development must be accompanied by ethical, legal, and institutional reflection. Public cultural institutions were presented as having a leading role in preventing unchecked or exploitative uses of AI, protecting minority cultural expressions, and supporting rights management, respectful attribution, and responsible reuse. The three co-leaders brought complementary perspectives to these questions: Lartillot through signal processing and computational music analysis, Mattes through musicological and archival research, and Romarheim Haugen through library infrastructure and policy. As an opening overview, the session established several of the themes developed throughout the remainder of Day 2.

• Hans-Hinrich Thedens, Olivier Lartillot & Lars Løberg Monstad (2026). Folk music and Hardanger fiddle transcription. doi: https:/www.uio.no/ritmo/english/news-and-events/events/conferences/2026/daim/index.html. Fulltekst i vitenarkiv Vis sammendrag

  The Norwegian Collection of Folk Music was founded not simply to preserve tradition, but to enable scientific study based on sound rather than manuscripts alone. Its first director, Olav Gurvin, even collaborated with the Institute of Physics on an early melody-writing device. Field collecting later documented regional practices, while editorial work produced seven volumes of Hardanger fiddle tunes followed by volumes for standard fiddle. Since 2014, the original reel-to-reel tapes have been digitized and a searchable catalogue placed online. The collaboration with RITMO first addressed long tapes containing sequences of tunes separated by spoken announcements. Existing metadata were combined with energy measurements and Apple’s estimates of speech, music, violin, and singing. Rule-based processing proposed boundaries, which a research assistant verified and associated with catalogue information. This divided 343 one-to-two-hour tapes into individual tunes, currently disseminated, as a first prototype, through the Open Science Framework. 19 The second part focused on automatic transcription of orally transmitted Hardanger fiddle music. Rather than treating a conventional score as the sole endpoint, the team aims to produce time-aligned representations retaining note onsets, offsets, and continuous- frequency pitch, eventually aligned with notation or displayed as a proportionally spaced score. This matters because flexible beat durations and microtonal intonation can be ob- scured by conventional notation. A compact model adapted from the piano-transcription system Transkun proposes polyphonic events. Transfer from piano transcription is facil- itated by the instrument’s stable pitch and frequent two-string textures, but the small dataset—119 manually annotated tunes—requires a reduced architecture and dedicated augmentation. Psychoacoustic post-processing refines pitches beyond the 440-Hz equal- tempered grid and reconstructs symbolic note endings masked by resonance, for instance by linking melody notes above a sustained drone. The transcriptions can support studies of intonation, rhythm, and recurrent structure; metadata generation and search; and learning and performance. Beat tracking and large-scale deployment remain ongoing work.

• Olivier Lartillot (2026). MishMash WP6 and the MusikkData infrastructure project. doi: https:/nfdi4culture.de/news/details/international-musicology-workshop.html. Fulltekst i vitenarkiv
• Olivier Lartillot (2026). Building Connected Collections of Norwegian Music: From Segmentation and Transcription to Interoperable Data and AI-Enabled Discovery. doi: https:/www.dariah.eu/activities/working-groups/artificial-intelligence-and-music-aim/. Fulltekst i vitenarkiv
• Olivier Lartillot & Lars Løberg Monstad (2026). Towards automated transcription of Hardanger fiddle performances. doi: https:/dlfm.web.ox.ac.uk/sites/default/files/dlfm/documents/media/lartillot_poster_dlfm26.pdf. Fulltekst i vitenarkiv
• Olivier Lartillot & Lars Løberg Monstad (2026). Hybrid AI for Expert-Assisted Transcription of Norwegian Hardanger Fiddle Music. doi: 10.5281/zenodo.23009316. Fulltekst i vitenarkiv Vis sammendrag

  The traditional Norwegian Hardanger fiddle repertoire is a rich oral heritage. Existing score transcriptions are scarce and rarely capture essential performance features, particularly ornamentation, microtonal intonation, two-string textures, and systematic beat unevenness. We develop an innovative AI-assisted transcription framework producing detailed, performance-aligned MIDI representations that preserve fine- grained timing and pitch. The neural front-end adapts a state-of-the-art deep-learning model for polyphonic music transcription to our small but growing dataset of expert annotations. It progressively learns from expert corrections, providing increasingly robust results, as reflected by a rising F1 score. The model is complemented by signal processing, musicological knowledge, and cognitive principles addressing repertoire-specific challenges such as non-standard tuning, sympathetic-string resonances, sustained drones, and symbolic note offsets. Acoustic analysis refines pitch beyond the conventional twelve-tone MIDI grid, while tune-specific pitch distributions organise the performed notes into musically meaningful categories. These results are transformed into a new score representation proportionally aligned with the recording. Colours make stable “micromodal” pitch categories visible and highlight ambiguous notes or pitches lying outside those categories. This makes the score useful not only for reading and performance, but also for checking AI output, exploring intonation and texture, and identifying recurring patterns. This expert-in-the-loop workflow drastically reduces transcription correction time, with the aim of progressing towards full automation. The resulting transcriptions and scores will support performance, education, archive exploration, corpus research, and future AI methods for musical heritage. This original hybrid AI approach also demonstrates the limitations of purely machine-learning approaches to music transcription.

• Olivier Lartillot (2026). AI-powered curation and enrichment of music collections: (Norwegian) (folk) music collection(s). doi: 10.5281/zenodo.23009316. Fulltekst i vitenarkiv
• Vendrix, Philippe, Ahrendt, Rebekah, Judith Irmela Haug & Olivier Lartillot (2026). Musicology and Artificial Intelligence. doi: 10.5281/zenodo.20639784. Fulltekst i vitenarkiv Vis sammendrag

  This report provides an overview of Artificial Intelligence in European musicological research, addressed to musicologists, librarians, archivists, curators, educators, research-policy officers, infrastructure coordinators, and tool developers. Moving from conceptual foundations to institutional implications, it maps the affordances and limitations of AI-based models and tools for the discipline, while situating them within the European normative landscape defined by the ALLEA European Code of Conduct for Research Integrity, the EU Artificial Intelligence Act, and the UNESCO Recommendation on the Ethics of Artificial Intelligence. The six complementary sections successively address: (1) methodological foundations, clarifying notions of model, tool, and critical posture, and surveying audio-, image-, encoding-, and language-model-based applications such as automatic music transcription, optical music recognition, score-audio alignment, automated analysis, reconstruction, and LLM prompt­ing; (2) data infrastructure, covering FAIR/CARE governance, ontologies and persistent identifiers, European federated infra­structures, auditory digital twins, and risks of fragmentation, representational bias, quality variability, and copyright; (3) a continuation of the disciplinary stakes involved in source and corpus processing; (4) musicological education in the age of AI, including curricular redesign, AI literacy, and design models such as the Two-Lane Approach; (5) professional practices and responsibilities, examining scholarly credibility, intellectual property, academic integrity frameworks, cultural and creative industry collaborations, institutional and legal risks, gender equality, and lessons from organised labour; and (6) best practic­es, articulating methodological pluralism against technological determinism and formalising principles of ethical reflexivity, human oversight, explainability, reproducibility, and accountability.

• Olivier Lartillot, Pierre Beauguitte, Ingrid Romarheim Haugen, Alexander Refsum Jensenius & Yngvar Kjus (2026). Symposium on Digital and AI Musicology. Fulltekst i vitenarkiv Vis sammendrag

  Current research on the use of computational methods and AI in music libraries and musicology. This event brings together key members of the European consortium EarlyMuse, in coordination with MishMash – Centre for AI & Creativity, particularly its thematic area on cultural heritage. It presents recent work on digital archives, music information retrieval, and AI-based approaches to musicological and heritage research. Participants will also have the opportunity to engage with new and insightful reviews in computational musicology. The programme introduces and discusses a range of core technologies—including music transcription (from both score images and audio), music analysis, and the transformative potential of rich metadata—while highlighting strategies for interconnecting disparate music collections so that their metadata can meaningfully interact.

• Olivier Lartillot & Alexander Refsum Jensenius (2026). DAIM 2026: Symposium on Digital and AI Musicology. doi: 10.5281/ZENODO.22643770. Fulltekst i vitenarkiv Vis sammendrag

  This report presents extended summaries of the presentations and discussions at the DAIM 2026 Symposium on Digital and AI Musicology, held on 8–9 June 2026 at the National Library of Norway and the University of Oslo. Organized by RITMO, the MishMash Centre for AI and Creativity, and the National Library of Norway, the symposium brought together the COST Action EarlyMuse and MishMash Work Package 6 on AI for Cultural Heritage. The contributions examine how computational methods and artificial intelligence can support the preservation, transcription, analysis, discovery, and interconnection of musical heritage. Topics include digital music collections, Optical Music Recognition, audio transcription, chant and Sámi yoik, musical similarity and pattern discovery, Linked Open Data, metadata standards, and sustainable research infrastructures. Discussions address scholarly validation, provenance, cultural context, and the rights of creators and communities. Compiled and edited by Olivier Lartillot, the report synthesizes presentation abstracts, slides, and recorded talks. It includes introductions to both days, presentation and panel summaries, links to related resources, contributor biographies, and the symposium programme, alongside a tribute to Eleanor Selfridge-Field.

• Sunniva Monclair Bøe, Elise Koppang Frøjd, Alexander Refsum Jensenius, Diego Marin-Bucio, Benedikte Wallace & Olivier Lartillot (2026). Debatten raser om KI i kulturfeltet: Trenger vi egentlig musikere, forfattere og filmprodusenter nå? [Journal]. Forskning.no. Fulltekst i vitenarkiv Vis sammendrag

  Norske forskere vil finne ut hvordan kreative utøvere kan være med på å utvikle kunstig intelligens – og motsatt.

• Olivier Lartillot & Ingrid Romarheim Haugen (2025). Envisioning a National Digital Platform: Interconnecting Music Archives, Data, and Discovery. doi: https:/dhko.no/files/DHKO2025_Sammendrag.pdf. Fulltekst i vitenarkiv Vis sammendrag

  The National Library of Norway hosts a range of digital music collections that are currently not readily accessible and are dispersed across various databases, encompassing formats such as music recordings, sheet music, and related materials like texts, videos, and interviews. We envision creating a centralised infrastructure that brings these collections together on a high-standard platform, adhering to the latest metadata standards to enhance search capabilities and facilitate integration with international networks. This would allow users, even those querying from other systems abroad, to access music data and related information seamlessly. Additionally, we aim to extract musical information from diverse formats—such as audio recordings and sheet music images—promoting interconnectivity between these modalities. This foundational work will also pave the way for incorporating emerging AI technologies for music analysis and search in the future. To inform our vision, we are surveying music researchers in Norway to gain insights into the needs of potential users. This infrastructure represents the initial step toward developing more user-friendly interfaces targeting a broader audience, ultimately extending the impact of music research

• Vendrix, Philippe, Ahrendt, Rebekah, Judith Irmela Haug & Olivier Lartillot (2025). Musicology as Data Science and Heritage Science. doi: 10.5281/zenodo.17913186. Fulltekst i vitenarkiv Vis sammendrag

  This collective guide explores the transformative intersections of musicology with data science and heritage science in theEuropean context. It positions musicological research as both a model for and contributor to broader cultural heritage, analyzingits tangible and intangible dimensions. The report  reviews advances in research infrastructures, including the European Open Science Cloud, ECHOES, and E-RIHS, highlighting challenges and opportunities for integrating music into transnational heritage frameworks. It surveys the complexity of music data — notation, audio, metadata, and visual sources — and emphasizes standardized methods for cataloging and preservation. The text critically assesses legal, policy, and technical barriers to digitizing and reusing musical materials, advocating for legal reforms and open science principles. New publication paradigms are proposed, leveraging digital tools to expand access and impact. The role of musicologists in public engagement, education, and fostering research communities is underscored as crucial for sustainability. Best practices and collaborative models link musicological data to broader digital humanities and preservation strategies. Ultimately, the guide calls for active participation in European policy and research networks to enhance musicology’s role in shaping and disseminating cultural heritage for future generations.

• Olivier Lartillot (2025). Computational music analysis. Fulltekst i vitenarkiv
• Thomas Wosch, Bastian Vobig & Olivier Lartillot (2025). Human Interaction assessment and Generative segmentation in Health & Music. doi: https:/www.youtube.com/watch?v=I4jaZIzX0wg. Fulltekst i vitenarkiv Vis sammendrag

  Improvisation in music therapy has been shown to be an effective technique for engaging clients in emotionally rooted (inter)action to treat affective disorders such as major depression (Aalbers et al., 2017; Erkkilä et al., 2011). During improvisation, however, a variety of musical information is exchanged, resulting in a highly complex musical and interpersonal situation. While traditional models of music therapy analysis emphasise aural analysis and assessment of single sessions (Bruscia, 1987), more recent and elaborated methods, such as microanalysis, focus on the detailed development of improvisation sessions (Wosch, 2021; Wosch & Erkkilä, 2016), which comes at the cost of a more time-consuming application process. Digital processing, as in music information retrieval and machine learning, seems promising to accelerate the analysis process, but requires considerable preliminary work in data preprocessing and formalisation of the high-level concepts used in music therapy to develop a suitable dataset for model training. Moreover, additional benefits of digital processing comprehend a more detailed and precise analysis of musical data.

• Marina Sudo, Michelle Ziegler, Miriam Akkermann & Olivier Lartillot (2025). Towards Collaborative Analysis: Kaija Saariaho’s Io (1986–87). Fulltekst i vitenarkiv
• Marina Sudo & Olivier Lartillot (2025). Contemporary Music Analysis and Auditory Memory: The Use of Computational Tools as an Aid for Listening. doi: https:/fabricadesites.fcsh.unl.pt/ncmm/ncmm-2025-program/. Fulltekst i vitenarkiv Vis sammendrag

  Music analysis involves categorising and interpreting sonic elements to uncover the structure and meaning of a work. In contemporary music studies, analysts often face methodological challenges in this process, especially when dealing with works that contain high degrees of complexity and ambiguity in terms of timbre, texture and temporal structure. This paper proposes a methodological model for analysing spatiotemporal complexities commonly observed in contemporary repertoires, utilising computational tools to enhance auditory memory and expand interpretative possibilities. Auditory memory plays a pivotal role in aural analysis, an approach that serves as a valuable alternative or complement to traditional score-based analysis. Rooted in Pierre Schaeffer’s typomorphology of objets sonores and the work of other analysts in electroacoustic music studies, the general principles of aural analysis can be outlined in a three-step process: 1) attentive listening to the acoustic properties of sounds, 2) describing and categorising their sonic variations, and 3) assessing their functions within a large-scale formal structure. Computational sound visualisation tools are frequently employed in this process to assist in transcribing and retaining musical events that are either absent from the score or difficult to interpret aurally due to textural complexities and/or timbral elusiveness. Despite their increasing use, however, the full potential of these tools remains largely unexplored in contemporary music studies. By digitally decomposing the transformation processes of ambiguous musical flows and supporting the organisation and structuring of auditory memory, computational analysis of audio data and various visualisation methods can deepen our understanding of both local sonic morphology and large-scale formal trajectory. In line with these considerations, the paper investigates how specialised computer interfaces can facilitate music analytical processes. Two research questions guide this investigation: 1) How can we analyse a stream of sonic textures; and 2) How can we outline the formal structure of a work that embraces extremes of sonic energy and polyrhythmic intricacy? To explore these questions, we have developed muScope, a new computer program that enables users to browse within high-resolution sonograms in tandem with a range of graphical representations capturing audio, timbral, rhythmic and structural descriptions. The analysis of spectral “fluctuations” allows for the identification of rapid pulsations at the middle ground between rhythm and timbre. Self-similarity matrix representations can serve as a tool for outlining the structural division of the audio data based on various sonic attributes. We integrate these visual representations into an analytical workflow designed to support the construction of a composition’s formal structure. Our methods are demonstrated through an analysis of excerpts from Kaija Saariaho’s Io for large ensemble and electronics (1986–87) and Raphaël Cendo’s Corps for piano and ensemble (2015). This integrated analytical approach offers new insights into the interplay between musical perception, memory and analytical interpretation using digital tools.

• Anna-Maria Christodoulou, Kyrre Glette, Olivier Lartillot & Alexander Refsum Jensenius (2025). MusiQAl: Music Question Answering through Audio-Video fusion. doi: https:/ismir2025.ismir.net/program-detailed-schedule. Fulltekst i vitenarkiv
• Olivier Lartillot (2025). Computational Music Analysis Applied to Music Therapy Improvisation. doi: https:/ifas.thws.de/fileadmin/user_upload/250917_HIGH-M_Symposium_Programme_updated.pdf. Fulltekst i vitenarkiv
• Olivier Lartillot (2025). Computational Music Analysis: Toolbox and application to music psychology & therapy. Fulltekst i vitenarkiv
• Anna-Maria Christodoulou & Olivier Lartillot (2025). A Multimodal Dataset of Greek Folk Music. doi: https:/dlfm.web.ox.ac.uk/2025-programme. Fulltekst i vitenarkiv
• Olivier Lartillot (2024). Visualisation of Bartok First String Quartet, 1st movement. Fulltekst i vitenarkiv
• Olivier Lartillot (2024). Successes and challenges of computational approaches for audio and music analysis and for predicting music-evoked emotion. Fulltekst i vitenarkiv Vis sammendrag

  Background Decades of research in computational sound and music analysis has led to a large range of analysis tools offering rich and diverse description of music, although a large part of the subtlety of music remains out of reach. These descriptors are used to establish computational models predicting perceived or induced emotion directly from music. Although the models can predict a significant amount of variability of emotions experimentally measured (Panda et al., 2023), further progress seems hard to achieve, probably due to the subtlety of music and of the mechanisms underlying the evocation of emotion from music. Aims An extensive but synthetic panorama of computational research in sound and music analysis as well as emotion prediction from music is presented. Core challenges are highlighted and prospective ways forward are suggested. Main contribution For each separate music dimension (dynamics, timbre, rhythm, tonality and mode, motifs, phrasing, structure and form), a synthetic panorama of the state of the art is evoked, highlighting strengths and challenges as well as indicating how particular sound and music features have been found to correlate with rated emotions. The various strategies for modelling emotional reactions to audio and musical features are presented and discussed. One common general analytical approach carries out a broad and approximate analysis of the audio recording based on simple mathematical models, describing individual audio or musical characteristics numerically. It is suggested that such loose approach might tend to drift away from commonly understood musical processes and to generate artefacts. This vindicates a more traditional musicological approach based on a focus on the score or approximations of it – through automated transcription if necessary – and a reconstruction of the types of traditional representations commonly studied in musicology. I also argue for the need to closely reflect the way humans listen to and understand music, inspired by a cognitive perspective. Guided by these insights, I sketch the idea of a complex system made of interdependent modules, founded on sequential pattern inference and activation scores not based on statistical sampling. I also suggest perspectives for the improvement of computational prediction of emotions evoked by music. Discussion and conclusion Further improvements of computational music analysis methods, as well as emotion prediction, seem to call for a change of modelling paradigm. References R. Panda, R. Malheiro, R. Paiva, "Audio Features for Music Emotion Recognition: A Survey", IEEE Transactions on Affective Computing, 14-1, 68-88, 2023.

• Olivier Lartillot (2024). Introduction to the MiningSuite toolbox. Fulltekst i vitenarkiv
• Olivier Lartillot (2024). KI-verktøy for håndtering, transkribering og analyse av musikkarkiver. Fulltekst i vitenarkiv Vis sammendrag

  Jeg presenterer en rekke verktøy utviklet i samarbeid med Nasjonalbiblioteket. AudioSegmentor deler automatisk båndopptak i individuelle musikkstykker. Dette verktøyet forenklet digitaliseringen av Norsk folkemusikksamling. Vi bruker avanserte dyp læringsmetoder for å skape et banebrytende automatisk musikktranskriberingssystem, MusScribe, først finjustert for Hardingfele, og nå gjort tilgjengelig for musikkarkivprofesjonelle for et bredt spekter av musikk. Jeg diskuterer også våre pågående fremskritt innen den automatiserte musikologiske analysen av folkemusikkstykker og omfattende samlinger.

• Michelle Ziegler, Marina Sudo, Miriam Akkermann & Olivier Lartillot (2024). Towards Collaborative Analysis: Kaija Saariaho’s IO. Fulltekst i vitenarkiv
• Hans-Hinrich Thedens & Olivier Lartillot (2024). The Norwegian Catalogue of Folk Music Online. Fulltekst i vitenarkiv
• Lars Løberg Monstad & Olivier Lartillot (2024). muScribe: a new transcription service for music professionals. Fulltekst i vitenarkiv
• Mats Sigvard Johansson & Olivier Lartillot (2024). Automated transcription of Hardanger fiddle music: Tracking the beats. Fulltekst i vitenarkiv
• Lars Løberg Monstad & Olivier Lartillot (2024). Automated transcription of Hardanger fiddle music: Detecting the notes. Fulltekst i vitenarkiv
• Olivier Lartillot (2024). MIRAGE Closing Seminar: Digitisation and computer-aided music analysis of folk music. Fulltekst i vitenarkiv Vis sammendrag

  One aim of the MIRAGE project is to conceive new technologies allowing to better access, understand and appreciate music, with a particular focus on Norwegian folk music. This seminar presents what has been achieved during the four years of the project, leading in particular to the digital version of the Norwegian Catalogue of Folk Music. We are also conceiving tools to automatically transcribe audio recordings of folk music. More advanced musicological applications are discussed as well. To conclude, we introduce the new spinoff project, called muScribe, aimed at the development of transcription services, for a broad range of music, besides folk music, in a first stage tailored to professional organisations such as archives, publishers and producers.

• Olivier Lartillot (2024). Overview of the MIRAGE project. Fulltekst i vitenarkiv
• Olivier Lartillot (2024). Harmonizing Tradition with Technology: Enhancing Norwegian Folk Music through Computational Innovation. Fulltekst i vitenarkiv Vis sammendrag

  My work involves developing computational tools to safeguard and elevate the cultural significance of music repertoires, with a focus on a cooperative project with the National Library of Norway related to their collection of Norwegian folk music. Our first phase centered on transforming unstructured audio tapes into a systematic dataset of melodies while ensuring its access and longevity through efficient data management and linking with other catalogues. Our core activity involves transcribing audio recordings into scores, comparing the traditional manual method with our modern attempts towards automation. Providing detailed performance notation, the close alignment between scores and audio recordings will help improve comprehension and overall accessibility, as well as a more advanced structuring of the collection. Challenges arose when incorporating this music into the International Inventory of Musical Sources (RISM) database due to the incompatible 'incipit' concept, unfitting genres like Hardanger fiddle folk music. We suggest innovative generalisations for this concept. Moreover, we're creating techniques to digitally dissect the musical corpus, aiming to extract key features of each tune. This initiative not only serves as an alternative to incipits but also provides novel metadata formats, increasing the usability and connectivity within its content and with other databases.

• Anna-Maria Christodoulou, Sagar Dutta, Olivier Lartillot, Kyrre Glette & Alexander Refsum Jensenius (2024). Exploring Convolutional Neural Network Models for Multimodal Classification of Expressive Piano Performance. Fulltekst i vitenarkiv
• Olivier Lartillot (2023). Towards a Comprehensive Modelling Framework for Computational Music Transcription/Analysis. Fulltekst i vitenarkiv Vis sammendrag

  Computational music analysis, still in its infancy, lacking overarching reliable tools, can be seen at the same time as a promising approach to fulfill core epistemo- logical needs. Analysis in the audio domain, although approaching music in its entirety, is doomed to superficiality if it does not fully embrace the underlying symbolic system, requiring a complete automated transcription and scaffolding of metrical, modal/harmonic, voicing and formal structures on top of the layers of elementary events (such as notes). Automated transcription enables to get over the polarity between sound and music notation, providing an interfacing semiotic system that combines the advantages of both domains, and surpassing the limitation of traditional approaches based on graphic representations. Deep learning and signal processing approaches for the discretisation of the continuous signal are compared and discussed. The multi-dimensional music transcription and analysis framework (where both tasks are actually deeply intertwined) requires to take into account the far-reaching interdependencies between dimensions, for instance between motivic and metrical analysis. We propose an attempt to build such a comprehensive framework, founded on general musical and cognitive principles and an attempt to build music analysis capabilities through a combina- tion of simple and general operators. The validity of the analyses is addressed in close discussion with music experts. The potential capability to produce valid analyses for a very large corpus of music would make such a complex system a potentially relevant blueprint for a cognitive modelling of music understanding. We try to address a large diversity of music cultures and their specific challenges: among others, maqam modes (with Mondher Ayari), Norwegian Hardanger fiddle rhythm (with Mats Johansson and Hans-Hinrich Thedens), djembe drumming from Mali (with Rainer Polak) or electroacoustic music (Towards a Toolbox des objets musicaux, with Rolf Inge Godøy). We aim at making the framework fully transparent, collaborative and open.

• Laura Bishop, Simon Høffding, Olivier Serge Gabriel Lartillot & Bruno Laeng (2023). Mental effort and expressive interaction in expert and student string quartet performance. Fulltekst i vitenarkiv
• Olivier Lartillot (2023). Computational audio and musical features extraction: from MIRtoolbox to the MiningSuite. Fulltekst i vitenarkiv
• Anna-Maria Christodoulou, Olivier Lartillot & Christina Anagnostopoulou (2023). Computational Analysis of Greek Folk Music of the Aegean. Fulltekst i vitenarkiv
• Olivier Lartillot (2023). Dynamic Visualisation of Fugue Analysis, Demonstrated in a Live Concert by the Danish String Quartet. Fulltekst i vitenarkiv
• Olivier Lartillot (2023). Towards a comprehensive model for computational music transcription and analysis: a necessary dialog between machine learning and rule-based design? Fulltekst i vitenarkiv
• Olivier Lartillot & Lars Løberg Monstad (2023). MIRAGE - A Comprehensive AI-Based System for Advanced Music Analysis. Fulltekst i vitenarkiv
• Laura Bishop, Simon Høffding, Bruno Laeng & Olivier Lartillot (2023). Mental effort and expressive interaction in expert and student string quartet performance. Fulltekst i vitenarkiv
• Olivier Lartillot, Dana Swarbrick, Finn Upham & Carlos Eduardo Cancino-Chacón (2023). Video visualization of a string quartet performance of a Bach Fugue: Design and subjective evaluation. Fulltekst i vitenarkiv
• Olivier Lartillot (2023). MIRAGE Symposium #2: Music, emotions, analysis, therapy ... and computer. Fulltekst i vitenarkiv Vis sammendrag

  The 2nd MIRAGE Symposium covers a broad range of topics related to the MIRAGE project, mainly related to music and emotion, music cognition in general, music analysis and music therapy. Featuring two keynotes by Patrik Juslin and Didier Grandjean.

• Clemens Maidhof, Kat Agres, Jörg Fachner & Olivier Lartillot (2023). Intra- and inter-brain coupling during music therapy. Fulltekst i vitenarkiv
• Thomas Wosch, Bastian Vobig, Olivier Lartillot & Anna-Maria Christodoulou (2023). HIGH-M (Human Interaction assessment and Generative segmentation in Health and Music). Fulltekst i vitenarkiv
• Olivier Lartillot (2023). Music Therapy Toolbox, and prospects. Fulltekst i vitenarkiv
• Olivier Lartillot, Hans-Hinrich Thedens, Olav Luksengård Mjelva, Anders Elovsson, Lars Løberg Monstad & Mats Sigvard Johansson [Vis alle 8 forfattere av denne artikkelen] (2023). Norwegian Folk Music & Computational Analysis. Fulltekst i vitenarkiv Vis sammendrag

  As a prélude for Norway's Constitution Day, this special event celebrated the Norwegian folk music tradition, showcasing our new online archive and demonstrating the richness of Hardanger fiddle music, with live performance. One aim of the project is to conceive new technologies allowing to better access, understand and appreciate Norwegian folk music. In this event, we introduced a new online version of the Norwegian Folk Music Archive and discuss underlying theoretical and technical challenges. A live concert/workshop, with the participation of Olav Luksengård Mjelva, offered a lively introduction to Hardanger fiddle music and its elaborate rhythm. The interests and challenges of automated transcription and analysis were discussed, with the public release of our new software Annotemus. The symposium was organised in the context of the MIRAGE project (RITMO, in collaboration with the National Library of Norway's Digital Humanities Laboratory).

• Olivier Lartillot & Lars Løberg Monstad (2023). Computational music analysis: Significance, challenges, and our proposed approach. Fulltekst i vitenarkiv Vis sammendrag

  Music is something that we mostly all appreciate, yet it remains a hidden and enigmatic concept for many of us. Music notation, in the form of music scores, facilitates practicing and enhances the understanding of the richness of musical works. However, acquiring musical scores for any music performance is a tedious and demanding task (called music transcription) that demands considerable proficiency. Hence the interest of computational automation. But music is not just notes, it is also melody, rhythm, themes, timbre, and very subtle aspects such as form. While many of us may not be consciously familiar with these concepts, they still have a subconscious influence on our aesthetic experience. Interestingly, it often happens that the more we consciously understand the underlying language of music, the more we tend to appreciate and enjoy it. Therefore, there is value in creating computational tools that can automate and enhance these types of analyses. The presenters' past work resulted in the creation of Matlab's MIRtoolbox, which measures a broad range of musical characteristics directly from audio through signal processing techniques. Currently, the MIRAGE project prioritises music transcription (with a particular focus on Norwegian folk music), blending neural-network-based deep learning with conventional rule-based models. Through this project, they highlight the importance of acknowledging the interconnectedness between all musical elements. Additionally, they have crafted animated visualisations to make analyses more accessible to the general public and are aiming to make music transcription technology available to the public, with support from UiO Growth House.

• Lars Løberg Monstad & Olivier Lartillot (2023). Automatic Transcription Of Multi-Instrumental Songs: Integrating Demixing, Harmonic Dilated Convolution, And Joint Beat Tracking. Fulltekst i vitenarkiv Vis sammendrag

  In the rapidly expanding field of music information retrieval (MIR), automatic transcription remains one of the most sought-after capabilities, especially for songs that employ multiple instruments. Musscribe emerges as a state-of-the-art transcription tool that addresses this challenge by integrating three distinct methodologies: demixing, harmonic dilated convolution, and joint beat tracking. Demixing is employed to isolate individual instruments within a song by separating overlapping audio sources, thus ensuring each instrument is transcribed distinctly. Beat tracking is then run as a parallel process to extract the joint beat and downbeat estimations. These processes results in an output midi file, which is then quantized using information derived from the beat tracking. As such, this method paves the way for more accurate and sophisticated analyses, bridging the gap between human and machine understanding of music. Together, these methodologies allow us to produce transcriptions that are not only accurate but also highly representative of the original compositions. Preliminary tests and evaluations showcase the potential in transcribing complex musical pieces with high fidelity, outperforming many contemporary tools in the market. This innovative approach not only has implications for music transcription but also for broader applications in audio analysis, remixing, and digital music production. The model has been instrumental in accelerating the composition process for several Norwegian television shows. Moreover, its efficacy can be observed in the Netflix series "A Storm for Christmas." Renowned composer Peter Baden harnessed this tool to enhance his workflow, proving the demand for innovative tools like this in the professional music industry.

• Olivier Lartillot, Rolf Inge Godøy & Anna-Maria Christodoulou (2022). Computational detection and characterisation of sonic shapes: Towards a Toolbox des objets sonores. Fulltekst i vitenarkiv Vis sammendrag

  Computational detection and analysis of sound objects is of high importance both for musicology and sound design. Yet Music Information Retrieval technologies have so far been mostly focusing on transcription of music into notes in a classical sense whereas we are interested in detecting sound objects and their feature categories, as was suggested by Pierre Schaeffer’s typology and morphology of sound objects in 1966, reflecting basic sound-producing action types. We propose a signal-processing based approach for segmentation, based on a tracking of the salient characteristics over time, and dually Gestalt-based segmentation decisions based on changes. Tracking of pitched sound relies on partial tracking, whereas the analysis of noisy sound requires tracking of larger frequency bands possibly varying over time. The resulting sound objects are then described based on Schaeffer’s taxonomy and morphology, expressed first in the form of numerical descriptors, each related to one type of taxonomy (percussive/sustained/iterative, stable/moving pitch vs unclear pitch) or morphology (such as grain). This multidimensional feature representation is further divided into discrete categories related to the different classes of sounds. The typological and morphological categorisation is driven by the theoretical and experimental framework of the morphodynamical theory. We first experiment on isolated sounds from the Solfège des objets sonores—which features a large variety of sound sources—before considering more complex configurations featuring a succession of sound objects without silence or with simultaneous sound objects. Analytical results are visualised in the form of graphical representations, aimed both for musicology and music pedagogy purposes. This will be applied to the graphical descriptions of and browsing within large music catalogues. The application of the analytical descriptions to music creation is also investigated.

• Olivier Lartillot (2022). The MIRAGE project: Unlocking new computational abilities in computational music analysis. Fulltekst i vitenarkiv
• Olivier Lartillot (2022). Computational music analysis: Application to music & emotion. Fulltekst i vitenarkiv
• Olivier Lartillot, Anders Elovsson, Mats Sigvard Johansson, Hans-Hinrich Thedens & Lars Alfred Løberg Monstad (2022). Segmentation, Transcription, Analysis and Visualisation of the Norwegian Folk Music Archive. Fulltekst i vitenarkiv Vis sammendrag

  We present an ongoing project dedicated to the transmutation of a collection of field recordings of Norwegian folk music established in the 1960s into an easily accessible online catalogue augmented with advanced music technology and computer musicology tools. We focus in particular on a major highlight of this collection: Hardanger fiddle music. The studied corpus was available as a series of 600 tape recordings, each tape containing up to 2 hours of recordings, associated with metadata indicating approximate positions of pieces of music. We first need to retrieve the individual recording associated with each tune, through the combination of an automated pre-segmentation based on sound classification and audio analysis, and a subsequent manual verification and fine-tuning of the temporal positions, using a home-made user interface. Note detection is carried out by a deep learning method. To adapt the model to Hardanger fiddle music, musicians were asked to record themselves and annotate all played note, using a dedicated interface. Data augmentation techniques have been designed to accelerate the process, in particular using alignment of varied performances of same tunes. The transcription also requires the reconstruction of the metrical structure, which is particularly challenging in this style of music. We have also collected ground-truth data, and are conceiving a computational model. The next step consists in carrying out detailed music analysis of the transcriptions, in order to reveal in particular intertextuality within the corpus. A last direction of research is aimed at designing tools to visualise each tune and the whole catalogue, both for musicologists and general public.

• Anne Danielsen, Guilherme Schmidt Câmara, Olivier Lartillot, Sabine Liliana Leske & Connor Spiech (2022). Musical rhythm. Behavioural, computational and neurophysiological perspectives. Fulltekst i vitenarkiv
• Olivier Lartillot & Mari Lilleslåtten (2021). Olivier Lartillot utvikler verktøy for å forstå musikk bedre. [Internet]. Det humanistiske fakultet UiO YouTube account. Fulltekst i vitenarkiv Vis sammendrag

  Kunstig intelligens kan hjelpe deg å forstå musikk bedre. UiO-forsker Olivier Lartillot jobber for at ny teknologi kan åpne folks ører for ny musikk.

• Olivier Lartillot & Mari Lilleslåtten (2021). Artificial intelligence can help you understand music better. [Internet]. RITMO News. Fulltekst i vitenarkiv Vis sammendrag

  Algorithms and technology have so far helped listeners to more of the same music. Now, UiO researchers are working on new technology that can get people interested in a greater musical variety.

• Anders Elovsson & Olivier Lartillot (2021). A Hardanger Fiddle Dataset with Performances Spanning Emotional Expressions and Annotations Aligned using Image Registration. Fulltekst i vitenarkiv Vis sammendrag

  This paper presents a Hardanger fiddle dataset “HF1” with polyphonic performances spanning five different emotional expressions: normal, angry, sad, happy, and tender. The performances thus cover the four quadrants of the activity/valence-space. The onsets and offsets, together with an associated pitch, were human-annotated for each note in each performance by the fiddle players themselves. First, they annotated the normal version. These annotations were then transferred to the expressive performances using music alignment and finally human-verified. Two separate music alignment methods based on image registration were developed for this purpose; a B-spline implementation that produces a continuous temporal transformation curve and a Demons algorithm that produces displacement matrices for time and pitch that also account for local timing variations across the pitch range. Both methods start from an “Onsetgram” of onset salience across pitch and time and perform the alignment task accurately. Various settings of the Demons algorithm were further evaluated in an ablation study. The final dataset is around 43 minutes long and consists of 19 734 notes of Hardanger fiddle music, recorded in stereo. The dataset and source code are available online. The dataset will be used in MIR research for tasks involving polyphonic transcription, score alignment, beat tracking, downbeat tracking, tempo estimation, and classification of emotional expressions.

• Olivier Lartillot & Stéphanie Weisser (2021). Roughness, Crackliness, Buzzingness, ...: Characterizations of Sonic Unsteadiness and Application to the Analysis of Traditional Music from Ethiopia, Kenya, Morocco and India. Fulltekst i vitenarkiv
• Aleksander Tidemann & Olivier Lartillot (2021). Interactive tools for exploring performance patterns in hardanger fiddle music. Fulltekst i vitenarkiv
• Olivier Lartillot, Erling Eliseus Guldbrandsen & Carlos Eduardo Cancino-Chacón (2021). Dynamics analysis, and application to a comparative study of Bruckner performances. Fulltekst i vitenarkiv
• Olivier Lartillot (2021). Presentation of MIRAGE project. Fulltekst i vitenarkiv
• Olivier Lartillot & Mats Sigvard Johansson (2021). Tracking beats in Hardanger fiddle tunes. Fulltekst i vitenarkiv
• Olivier Lartillot, Anders Elovsson & Olav Luksengård Mjelva (2021). A new software for computer-assisted annotation of music recordings, with a focus on transcription. Fulltekst i vitenarkiv
• Rolf Inge Godøy & Olivier Lartillot (2021). Acoustic substrates of musique concrète features: Towards a Toolbox de l'objet musical? Fulltekst i vitenarkiv
• Aleksander Tidemann, Olivier Lartillot & Mats Sigvard Johansson (2021). Towards New Analysis And Visualization Software For Studying Performance Patterns in Hardanger Fiddle Music. Fulltekst i vitenarkiv Vis sammendrag

  Analyzing musical performances is a challenging and emergent field of computational music research, aiming to reveal performance patterns and link them to musical contexts. There exists a modest amount of computational research on Hardanger fiddle performances. The MIRAGE research project is currently contributing to this scientific body, developing advanced MIR frameworks that build on recent musicological research. This paper presents the development and evaluation of two Max/MSP/Jitter software applications for music analysis and data visualization that integrate contemporary research perspectives on the complex rhythmical structuring of springar performances, investigating how we can design user-friendly computational tools that explore performance patterns in Hardanger fiddle music, in collaboration with MIRAGE. Based on a small questionnaire and a few operational tests, the study shows an interest in more effective software tools capable of revealing complex interrelations between musical dimensions in Hardanger fiddle performances. Additionally, the study highlights design considerations for tools aiming to increase the availability of computational music research in the field of musicology, such as cross-compatibility and integrated features that actively facilitate nuanced interpretation processes.

• Joachim Dalgard, Olivier Lartillot, Jonna Katariina Vuoskoski & Erling Eliseus Guldbrandsen (2021). Absorption - Somewhere between the heart and the brain. Fulltekst i vitenarkiv
• Olivier Lartillot & Petri Toiviainen (2020). Read about the Matlab MIRtoolbox. [Journal]. Young Acousticians Network (YAN) Newsletter. Fulltekst i vitenarkiv Vis sammendrag

  MIRtoolbox is a Matlab toolbox dedicated to the analysis of music and sound from audio recordings and to the extraction of musical features such as tonality, rhythm, or structures. It has also been used for non- musical applications, such as in Non Destructive Testing, and with non-audio signals. In this issue of the newsletter, the YAN discusses the MIRtoolbox with Olivier Lartillot (RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Norway) and Petri Toiviainen (University of Jyväskylä, Finland) You can also check out the MIRtoolbox website at: shorturl.at/oA038

• Olivier Lartillot & Marina Sudo (2025). AcousMuScope: Users' Guide. Universitetet i Oslo. doi: https:/www.uio.no/ritmo/english/projects/mirage/software/AcousMuScope/index.html. Fulltekst i vitenarkiv Vis sammendrag

  AcousMuScope is a new software for music analysis of audio recordings, focusing on the graphical interface to browse into the analyses.

• Grzegorz Joachimiak, Rebekah Ahrendt & Olivier Lartillot (2024). Endangered Musical Sources: Strategies for Safeguarding, Digitization, and International Collaboration. Report of Working Group 2 SOURCES, Wrocław, 22–24 May 2024. Zenodo. Fulltekst i vitenarkiv
• Anna-Maria Christodoulou, Christina Anagnostopoulou & Olivier Lartillot (2022). Computational Analysis of Greek folk music of the Aegean islands. National and Kapodistrian University of Athens. Fulltekst i vitenarkiv

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