Music

Over the last few days, we have run a toolbox hackathon between the three music-related research centres of excellence in the Nordic countries (MIB, MMBB, and RITMO). Researchers across all centres are actively developing various toolboxes, including myself and the Musical Gestures Toolbox (MGT). Due to all the MishMash things happening lately, I was unable to spend much time myself on hacking. Instead, I thought of the opportunity to test how GitHub Agents could assist. ...

Many musicians rely on basic tools in everyday life, including a tuner, a metronome, and a keyboard. I support the kids in a marching band, and I see that they rely on numerous poorly developed apps that differ between iOS and Android devices. Many of them also include ads, and some even play video ads in the middle of a tuning session. I wanted to see if I could develop some web-based apps to solve the problem. ...

One of my main goals while teaching MUS2640 – Sensing Sound and Music last semester was the integration of perspectives from musicology, psychology, and technology. The main point is to introduce the basics of music psychology and music technology, two disciplines that are quite distinct in most places, but which we at the University of Oslo have a tradition of combining. In this post, I explain the rationale. Three “logies” One of my main arguments is that we should consider the three “logies” involved in this course: ...

Yesterday, I wrote about the history and future of AI in general. Today, I am continuing my explorations by examining the role of AI in the arts and the impact of the arts on AI. The exploration is based on this notebook, with 60 sources collected by NotebookLM. AI in the Arts AI has a multi-generational history in the arts, transitioning from centuries-old mechanical automatons to symbolic rule-making and eventually to deep learning-based approaches. In the following, I will go through some of the works picked out by NotebookLM. Some of them I know well, others I hadn’t heard about before. A complete list of detected works is at the end of the post. ...

Since I am on a roll with cleaning up my Hugo-based blog, adding alt text, and supporting math writing, I also had to experiment with adding support for musical scores. For the Sensing Sound and Music book, I used music21 in Python after struggling to set up LilyPond. Both LilyPond (professional engraving) and music21 (symbolic analysis and MIDI generation) are powerful and could be integrated into a blog, but they usually require a server-side toolchain or extra installation. However, with the help of CoPilot, I discovered ABCjs, a lightweight client‑side library that provides rendering, playback, and MIDI export, which work well for static sites. ...

I have previously written about the differences between sound and audio and sound/light vs audio/video. In this short blog post, I problematize the concept “audiovisual”. Dictionary definitions The term “audiovisual” is ubiquitous. Unfortunately, for those of us working both on technology and psychology, it causes a lot of confusion. For example, consider the definition by Wikipedia: Audiovisual (AV) is electronic media possessing both a sound and a visual component, such as slide-tape presentations, films, television programs, corporate conferencing, church services, and live theater productions. ...

This semester, I have written a book with AI. I should emphasize the with in the previous sentence, because this has been an experience of co-creation between various large language models (LLMs) and me. This post details my approach to co-writing Sensing Sound and Music and reflects on the process. The need for a book The reason for my AI-based writing experiment was the need for a textbook for the course MUS2640 – Sensing Sound and Music at the University of Oslo. This is an introductory course for the bachelor’s students in our musicology program who want to major in music psychology and/or music technology. These are two distinct directions that are usually taught separately. However, at UiO, we have a strong tradition of combining psychological and technological perspectives on and with music (in the fourMs Lab and at RITMO), so I have argued that we need a foundation course showing the connections between the two disciplines. ...

It is somewhat embarrassing that I did not “discover” it before, but I only recently realized a striking parallel between the terms dancing and musicking. Music as a verb (“to music”) is only used by a small subset of music researchers, while “to dance” is universally accepted. Why? Musicking Christopher Small introduced the term musicking in the 1990s as a framework for understanding music as a social activity, using music as a verb (“to music”) rather than a noun (“music”). Though still not universal in musicology, the term has gained traction in music education and therapy, where participation in communal music practices is central. ...

One of my MUS2640 students asked which AI tool I had used to create the illustration on top of the textbook I have been developing for the course. The fact is, it isn’t AI-generated; it is a photo! I took the photo holding a 360-degree camera on my head while visiting the San Francisco Museum of Modern Art (SFMOMA) a couple of years ago. I was standing inside Olafur Eliasson’s One-way colour tunnel, located on a bridge inside the gallery. It is a fascinating three-dimensional light sculpture that no photo could capture. However, the 360-degree photo gives an impression of what it looks like: ...

In the second compulsory assignment in MUS2640 Sensing Sound and music, the students were tasked with a foundational exercise in audio analysis: creating spectrograms. Here I summarize how it went. The Assignment The core task was straightforward: Choose two distinct sound recordings. Students selected everything from musical instrument sounds and songs to environmental sounds like thunderstorms, boiling water, and animal calls. Generate visualizations in Sonic Visualiser. For each sound, they created a waveform (showing amplitude over time) and two types of spectrograms (showing frequency content over time). Compare linear and logarithmic scales. One spectrogram had to use a linear frequency axis and the other a logarithmic one. Write a reflection. Students described what they did and what they learned from comparing the sounds and the different visualizations. They were asked to do the exercise in the excellent open-source program Sonic Visualiser, but for those who wanted to do it in Python or MATLAB, that was fine. ...