Balint Laczko

• Laczko, Balint & Jensenius, Alexander Refsum (2021). Reflections on the Development of the Musical Gestures Toolbox for Python. I Kantan, Prithvi Ravi; Paisa, Razvan & Willemsen, Silvin (Red.), Proceedings of the Nordic Sound and Music Computing Conference. Aalborg Universitetsforlag. Fulltekst i vitenarkiv Vis sammendrag

  The paper presents the Musical Gestures Toolbox (MGT) for Python, a collection of modules targeted at researchers working with video recordings. The toolbox includes video visualization techniques such as creating motion videos, motion history images, and motiongrams. These visualizations allow for studying video recordings from different temporal and spatial perspectives. The toolbox also includes basic computer vision methods, and it is designed to integrate well with audio analysis toolboxes. The MGT was initially developed to analyze music-related body motion (of musicians, dancers, and perceivers) but is equally helpful for other disciplines working with video recordings of humans, such as linguistics, pedagogy, psychology, and medicine.

Se alle arbeider i Cristin

• Jensenius, Alexander Refsum & Laczko, Balint (2024). Video Visualization. Vis sammendrag

  This workshop is targeted at students and researchers working with video recordings. You will learn to use MG Toolbox, a Python package with numerous tools for visualizing and analyzing video recordings. This includes visualization techniques such as motion videos, motion history images, and motiongrams; techniques that, in different ways, allow for looking at video recordings from different temporal and spatial perspectives. It also includes some basic computer vision analysis, such as extracting quantity and centroid of motion, and using such features in analysis.MG Toolbox for Python is a collection of high-level modules for generating all of the above-mentioned visualizations and analyses. This toolbox was initially developed to analyze music-related body motion but is equally helpful for other disciplines working with video recordings of humans, such as linguistics, psychology, medicine, and educational sciences.

• Laczko, Balint (2023). Online guest lecture about The Hum - a real-time 3D audiovisual performance in Max.
• Laczko, Balint (2023). Two-part guest lecture about spatial audio and Ambisonics for MCT students.
• Laczko, Balint (2023). Video poster of the project entitled “The autophagic symphony – Unveiling the final rhythm”.
• Laczko, Balint (2023). Fluid.jit.plotter: a Max abstraction for plotting and querying millions of points fast using the Fluid Corpus Manipulation library.
• Laczko, Balint (2023). Workshop on Spatial Audio and Ambisonics.
• Laczko, Balint (2023). Guest lecture about granular synthesis with onset detection in Max.
• Jensenius, Alexander Refsum; Furmyr, Frida; Poutaraud, Joachim; Widmer, Marcus & Laczko, Balint (2022). The Musical Gestures Toolbox for Python. Vis sammendrag

  The Musical Gestures Toolbox for Python is a collection of tools for video visualization and video analysis.

• al Outa, Amani; Knævelsrud, Helene; Laczko, Balint & Jensenius, Alexander Refsum (2022). Winner of RRI-inspired transdisciplinary side quest call. [Internett]. Centre for Digital Life Norway. Vis sammendrag

  Centre for Digital Life Norway (DLN) is excited to congratulate the team behind the project “The autophagic symphony – Unveiling the final rhythm” as winner of DLN’s RRI-inspired transdisciplinary side quest call.

• Laczko, Balint & Jensenius, Alexander Refsum (2021). Reflections on the Development of the Musical Gestures Toolbox for Python. Vis sammendrag

  The paper presents the Musical Gestures Toolbox (MGT) for Python, a collection of modules targeted at researchers working with video recordings. The toolbox includes video visualization techniques such as creating motion videos, motion history images, and motiongrams. These visualizations allow for studying video recordings from different temporal and spatial perspectives. The toolbox also includes basic computer vision methods, and it is designed to integrate well with audio analysis toolboxes. The MGT was initially developed to analyze music-related body motion (of musicians, dancers, and perceivers) but is equally helpful for other disciplines working with video recordings of humans, such as linguistics, pedagogy, psychology, and medicine.

Se alle arbeider i Cristin