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:

I recently came across a video of the so-called Try not to headbang challenge, where the idea is to, well, not to headbang while listening to music. This immediately caught my attention. After all, I have been researching music-related micromotion over the last years and have run the Norwegian Championship of Standstill since 2012. Here is an example of Nath & Johnny trying the challenge: https://www.youtube.com/watch?v=-I4CBsDT37I As seen in the video, they are doing ok, although they are far from sitting still.

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:

Like many others, I bought a kayak during the pandemic, and I have had many nice trips in the Oslo fiord over the last year. Working at RITMO, I think a lot about rhythm these days, and the rhythmic nature of kayaking made me curious to investigate the pattern a little more. Capturing kayaking motion My spontaneous investigations into kayak motion began with simply recording a short video of myself kayaking.

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:

I came across a very interesting study on the Rhythmic swaying induced by sound in chimpanzees. The authors have shared the videos recorded in the study (Open Research is great!), so I was eager to try out some analyses with the Musical Gestures Toolbox for Matlab. Here is an example of one of the videos from the collection: The video quality is not very good, so I had my doubts about what I could find.

While generating the videograms of Bergensbanen, I discovered that Max/Jitter cannot export images from matrices that are larger than 32767 pixels wide/tall. This is still fairly large, but if I was going to generate a videogram with one pixel stripe per frame in the video, I would need to create an image file that is 1 302 668 pixels wide. This made me curious as to what type of limitations exist around images.

For some upcoming blog posts on videograms, I will start by explaining the difference between a motiongram and a videogram. Both are temporal (image) representations of video content (as explained here), and are produced almost in the same way. The difference is that videograms start with the regular video image, and motiongrams start with a motion image. So for a video of my hand like this: we will get this horizontal videogram:

While on paternity leave, I (finally) have time to do small projects that require little brain activity and lots of computation time. One of the things I have wanted to do for a long time is to create a videogram of Bergensbanen (which I briefly mentioned last year). This was a project undertaken by the Norwegian broadcast company (NRK), where they filmed (and broadcast live) the entire train trip from Bergen to Oslo.

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.

I just heard a talk called “Real-Time Synaesthetic Sonification of Traveling Landscapes” (PDF) by Tim Pohle and Peter Knees from the Department of Computational Perception (great name!) in Linz. They have made an application creating music from a moving video camera. The implementation is based on grabbing a one pixel wide column from the video, plotting these columns and sonifying the image. Interestingly enough, the images they get out (see below) of this are very close to the motiongrams and videograms I have been working on.