I participated in the conference The Aesthetics of Absence in Music of the Twenty-First Century at the Department of Musicology the last couple of days. Judith Lochhead started her keynote lecture with a clip from the movie The art of flying by Jan van Ijken. This is a beautiful short film based on clips of flocking birds: The art of flying from Jan van IJken on Vimeo. Of course, I wanted to see how some video visualizations would work, so I reached for the Musical Gestures Toolbox for Python.

I have been exploring different video visualizations as part of my annual stillstanding project. Some of these I post as part of my daily Mastodon updates, while others I only test for future publications. Most of the video visualizations and analyses are made with the Musical Gestures Toolbox for Python and structured as Jupyter Notebooks. I have been pondering whether skipping frames is a good idea. The 360-degree videos that I create visualizations from are shot at 25 fps.

Musique de Table is a wonderful piece written by Thierry de Mey. I have seen it performed live several times, and here came across a one-shot video recording that I thought it would be interesting to analyse: The test with some video visualization tools in the Musical Gestures Toolbox for Python. For running the commands below, you first need to import the toolbox in Python: import musicalgestures as mg I started the process by importing the source video:

After exploring some visualizations of kayaking, I was eager to see how a similar approach could work for walking. On a trip to the Norwegian mountains, specifically at Haugastøl, situated halfway between Oslo and Bergen, I strapped a GoPro Hero Black 10 on my chest and walked up and down a nearby hill called Storevarden. The walk was approximately 25 minutes up and down, and a fast-forward version of the video can be seen here:

I continue my testing of the new Musical Gestures Toolbox for Python. One thing is to use the toolbox on controlled recordings with stationary cameras and non-moving backgrounds (see examples of visualizations of AIST videos). But it is also interesting to explore “real world” videos (such as the Bergensbanen train journey). I came across a great video of flamenco dancer Selene Muñoz, and wondered how I could visualize what is going on there:

I came across a fantastic performance of a Bach prelude played on Boomwhackers by Les Objets Volants. https://www.youtube.com/watch?v=Y5seI0eJZCg It is really incredible how they manage to coordinate the sticks and make it into a beautiful performance. Given my interest in the visual aspects of music performance, I reached for the Musical Gestures Toolbox to create some video visualisations. I started with creating an average image of the video: This image is not particularly interesting.

Yesterday, PhD fellow Mojtaba Karbassi presented his research on impedance control in robotic drumming at RITMO. I will surely get back to discussing more of his research later. Today, I wanted to share the analysis of one of the videos he showed. Mojtaba is working on developing a robot that can play a double stroke drum roll. To explain what this is, he showed this video he had found online, made by John Wooton:

Researchers from AIST have released an open database of dance videos, and I got very excited to try out some visualization methods on some of the files. This was also a good chance to test out some new functionality in the Musical Gestures Toolbox for Matlab that we are developing at RITMO. The AIST collection contains a number of videos. I selected one hip-hop dance video based on a very steady rhythmic pattern, and a contemporary dance video that is more fluid in both motion and music.

I came across a high-speed recording of bowing on a violin string today, and thought it would be interesting to try to analyze it with the new version of the Musical Gestures Toolbox for Python. This is inspired by results from the creation of motiongrams of a high-speed guitar recording that I did some years ago. Here is the original video: From this I generated the following motion video: And from this we get the following motiongram showing the vertical motion of the string (time running from left to right):

Today the MIT Press journal Leonardo has published my paper entitled “Some video abstraction techniques for displaying body movement in analysis and performance”. The paper is a summary of my work on different types of visualisation techniques of music-related body motion. Most of these techniques were developed during my PhD, but have been refined over the course of my post-doc fellowship. The paper is available from the Leonardo web page (or MUSE), and will also be posted in the digital archive at UiO after the 6 month embargo period.

To allow everyone to watch their own synchronised spectrograms and motiongrams, I have made a small application called AudioVideoAnalysis. Download AudioVideoAnalysis for OS X (8MB) It currently has the following features: Draws a spectrogram from any connected microphone Draws a motiongram/videogram from any connected camera Press the escape button to toggle fullscreen mode Built with Max/MSP by Cycling ‘74 on OS X.5. I will probably make a Windows version at some point, but haven’t gotten that far yet.