I see that the concept of “tempo octave” is being used by some researchers in the music information retrieval (MIR) community. This is a confusing term from a musical perspective. Here I explain why this is a bad idea. Octaves An octave is a core term in (Western) music theory related to describing intervals, relationships between two notes (and tones!) with a frequency ratio of 2:1. Here is an example of an octave: ...
Today, we had the PhD defence of Olgerta Asko at RITMO. Her research is super interesting in itself (check out this feature story for an overview). This blog post is following up on one of the points she made during her trial lecture that I hadn’t thought about before: the difference between generalisation, inference, and deliberation. Towards deliberation Olga argued that current AI—here understood as large language models (LLMs)—are based on generalisation. They extract patterns from a lot of data and apply them broadly. As we have seen with recent commercial products and as I have explored in many ways on this blog, LLMs excel at this task. ...
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. ...
Due to MishMash, I am nowadays lecturing on AI, music, and creativity several times a week. I usually include a brief overview of machine learning history, mainly to explain that ChatGPT didn’t come out of nowhere but was the result of decades of research. To check that my story holds and to get a few more critical years and names in place. This blog post summarizes the brief history of AI to date. ...
I find Google’s NotebookLM to be one of the most powerful tools for researchers these days. Its core function is to work with the material you upload. Hence, it differs from ChatGPT and other tools that invent things on their own or search the web. It also has many different types of reports, including audio and video. But how accurate is it? Testing NotebookLM To test NotebookLM on some material I know very well, I decided to provide it with a collection of public data about myself, more specifically, what I have registered in the Norwegian NVA system, which is the database we use in Norway to register all our academic activities. This includes academic publications, public lectures, artistic works, interviews, etc. In my case, that contains around 1000 entries dating back to 2000. ...
Happy new year! As we move into 2026, I am currently undergoing a gradual shift in my academic life. I don’t have any new annual projects planned this year (like 365 Sound Actions or #StillStanding). This year is more about wrapping up old things and moving on with exciting new projects. I am more or less done with writing Still Standing, which summarizes 15 years of micromotion research. My AMBIENT project is also about to end, with sveral of my doctoral and postdoctoral fellows wrapping up their projects in the coming months. The plan is to write a book based on AMBIENT, too, summarizing my interest in indoor environments. However, book writing takes time, so it will probably take a few years before I have completed that project. ...
Yesterday, I wrote about how I cleaned up a lot of old mess here on this blog. This made me realize that I have not paid attention to creating alt text for all my images, that is, descriptive text that describes the content of the image. That is an enormous job when you have several thousand images on a blog like this. So I decided to ask CoPilot for help. Getting AI help CoPilot first suggested creating alt text based on the image file names. That could have been a good idea if the filenames had been descriptive. However, they are not (in general), hence I asked for a solution that would actually analyse the content of the images. After a series of iterations, we (CoPilot and I) ended up with a Python script that does the job. ...
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. ...
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: ...
Over the past few weeks, I have read several newspaper articles and seen numerous social media postings from researchers expressing skepticism about using AI in research. It is fine that some people prefer not to use new tools, but I don’t see why that should hinder the rest of us from exploring the possibilities that are out there. Many researchers do use AI tools, which I think is perfectly fine. The problem is that not many people talk about how they use the tools and reflect more on how they work. I think it is imperative that professors explore AI in various ways to see what works and what does not. After all, we are going to teach upcoming generations and lead the way in both development and use. We then need hands-on experience and qualified opinions. ...