Today, I ran a workshop called Video Visualization together with RITMO research assistant Joachim Poutaraud. The workshop was part of the Digital Scholarship Days 2023 organized by the University of Oslo Library, four days packed of hands-on tutorials of various useful things. Presentation slides made by Jupyter Notebook Joachim has done a fantastic job updating the Wiki with all the new things he has implemented in the toolbox. However, the Wiki is not the best thing to use in a workshop, it has too much information and would create an information overload for the participants.

For my annual Still Standing project, I am recording sensor data from my mobile phone while standing still for 10 minutes at a time. This is a highly curiosity-driven and data-based project, and part of the exploration is to figure out what I can get out of the sensors. I have started sharing graphs of the linear acceleration of my sessions with the tag #StillStanding on Mastodon. However, I wondered if this is the sensor data that best represents the motion.

I am doing some end of the year cleaning on my hard drive and just uploaded the Jupyter Notebook I used in the analysis of a mobile phone lying still earlier this year. For some future studies, I thought it would be interesting to explore the PDF export functionality from Jupyter. That worked very well except for that I didn’t get any title or author name on top: Then I found a solution on Stack Overflow.

What is the background “noise” in the sensors of a mobile phone? In the fourMs Lab, we have a tradition of testing the noise levels of various devices. Over the last few years, we have been using mobile phones in multiple experiments, including the MusicLab app that has been used in public research concerts, such as MusicLab Copenhagen. I have yet to conduct a systematic study of many mobile phones lying still, but today I tried recording my phone—a Samsung Galaxy Ultra S21—lying still on the table for ten minutes.

I wrote up the final report on the project MICRO - Human Bodily Micromotion in Music Perception and Interaction before Christmas. Now I finally got around to wrapping up the project pages. With the touch of a button, the project’s web page now says “completed”. But even though the project is formally over, its results will live on. Aims and objectives The MICRO project sought to investigate the close relationships between musical sound and human bodily micromotion.

After several years in the making, we finally “released” the Musical Gestures Toolbox for Python at the NordicSMC Conference this week. The toolbox is a collection of modules targeted at researchers working with video recordings. Below is a short video in which Bálint Laczkó and I briefly describe the toolbox: https://youtu.be/tZVX\_lDFrwc About MGT for Python The Musical Gestures Toolbox for Python includes video visualization techniques such as creating motion videos, motion history images, and motiongrams.

I am presenting at the Norwegian Forskerutdanningskonferansen on Monday, which is a venue for people involved in research education. I have been challenged to talk about why open research is better research. In the spirit of openness, this blog post is an attempt to shape my argument. It can be read as an open notebook for what I am going to say. Open Research vs Open Science My first point in any talk about open research is to explain why I think “open research” is better than “open science”.

Yesterday I gave a keynote lecture at the Munin Conference on Scholarly Publishing in Tromsø. This is an annual conference that gathers librarians, research administrators and publishers, but also some researchers and students. It is my first time to the conference, and found it to be a very diverse, interesting and welcoming group of people. Abstract Is it possible to do experimental music research completely openly? And what can we gain by opening up the research process from beginning to end?

My PhD student Qichao Lan is at SMC in Malaga this week, presenting the paper: Lan, Qichao, Jim Tørresen, and Alexander Refsum Jensenius. “RaveForce: A Deep Reinforcement Learning Environment for Music Generation.” Proceedings of the Sound and Music Computing Conference. Malaga, 2019. Download The framework that Qichao has developed runs nicely with a bridge between Jupyter Notebook and SuperCollider. This opens for lots of interesting experiments in the years to come.

I was at NTNU in Trondheim today, teaching a workshop on motion capture methodologies for the students in the Choreomundus master’s programme. This is an Erasmus Mundus Joint Master Degree (EMJMD) investigating dance and other movement systems (ritual practices, martial arts, games and physical theatre) as intangible cultural heritage. I am really impressed by this programme! It was a very nice and friendly group of students from all over the world, and they are experiencing a truly unique education run by the 4 partner universities.