Interested in doing a PhD in Amsterdam?

We are seeking a PhD candidate for the project Deep: Interpreting Deep Learning Models for Text and Sound Methods and Applications. This project is funded by an NWO Dutch Research Agenda grant to a consortium led by Dr. Willem Zuidema of the Institute for Logic, Language, and Computation (ILLC) at the University of Amsterdam (UvA).

 

This ambitious project aims to develop, apply, and fine-tune techniques to make modern deep learning models for text, speech, and music more transparent. 

 

In this PhD position you can combine insights from audio analysis and deep learning to help musicians play what they love. If you are excited about doing this kind of research in an interdisciplinary environment with smart and friendly colleagues and a strong industrial collaboration, then you may want to join us.

All information on how to apply can be found here.

Deadline for applications is 21 July 2022.

What makes music catchy?

Often you only need to hear a few seconds of music, to recognize a song. There's a good chance it was a very catchy tune. Computational musicologist Ashley Burgoyne (Music Cognition Group, University of Amsterdam) reveals what makes a song catchy.

Burgoyne, J. A., Bountouridis, D., Balen, J. van, & Honing, H. (2013). Hooked: A Game For Discovering What Makes Music Catchy. In A. De Souza Britto, F. Gouyon, & S. Dixon (Eds.), Proceedings of the International Society for Music Information Retrieval Conference (pp. 245–250). Curitiba, Brazil. [pdf]

Interested in bridging data science and music research during a PhD at the UvA?

The UvA Data Science Centre seeks to accelerate data driven research within the UvA. Part of that mission is to foster interdisciplinary research. Specifically, in this call, the UvA aims to foster research into new data science methods that help to tackle hard challenging problems in a given domain. Such interaction is realized through joint supervision of the proposed PhD project: one supervisor with core expertise in data science methods, the other with core expertise in the domain problem. 

For details on the PhD-program see here.  

If interested, please contact MCG before September 1, 2021.

Deadline for final applications:  September 23, 2021,17:00.

How universal is music?

Inuit throat singers (Source: Flickr)

Tomorrow an elaborate study addressing this question will be published in Science (Mehr et al., 2019). It consists of 17 pages main text and roughly 100 pages of supplementary materials. It will give all of us a lot to read and think about. (Although the Science paper is embargoed until tomorrow afternoon, in Amsterdam we discussed the paper this week based on the pre-publication made available through PsyArXiv.)

(Source: pre-publication)

The study (i.e. pre-publication) presents several interesting findings, some as expected, others very intriguing. First of all, it provides convincing evidence that music is indeed a universal phenomenon: it can be found in virtually all societies and varies more within than between societies. Secondly, the study shows that there are often clear relations between the form of the music (its musical structure) and its function – the context in which it is used. For instance, being a lullaby or healing song. And, even more interesting, “these patterns do not consist of concrete acoustic features, such as a specific melody or rhythm, but rather of relational properties such as accent, meter, and interval structure.” (Mehr et al., 2019, p.13). Clearly, universal features of human musicality (i.e. the capacity for music; Honing et al., 2015) lead people to produce and enjoy songs with certain kinds of rhythmic or melodic patterning that naturally go with certain moods.



P.S. Many media will cover the publication in the next few days. For a coverage in Dutch see, e.g. De Volkskrant and NRC.

Honing, H., ten Cate, C., Peretz, I., & Trehub, S. E. (2015). Without it no music: cognition, biology and evolution of musicality. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 370(1664), 20140088. https://doi.org/10.1098/rstb.2014.0088  

Mehr, S. A., Singh, M., Knox, D., Ketter, D. M., Pickens-Jones, D., Atwood, S., … Glowacki, L. (2019). Universality and diversity in human song. Science, 366 (21 November 2019), 1–17. https://science.sciencemag.org/cgi/doi/10.1126/science.aax0868 
[For an interactive version of the songs described, see http://themusiclab.org/nhsplots ]

What makes us musical animals? (ISMIR 2019 Keynote @TUDelft)

[N.B. Starts around 06:00]

What makes us musical animals, a one hour keynote at ISMIR 2019:

"We are all born with a predisposition for music, a predisposition that develops spontaneously and is refined by listening to music. Nearly everyone possesses the musical skills essential to experiencing and appreciating music. Think of “relative pitch,” recognizing a melody separately from the exact pitch or tempo at which it is sung, and “beat perception,” hearing regularity in a varying rhythm. Research shows that all humans possess the trait of musicality. We are a musical species — but are we the only musical species? Can there be musical machines? In his presentation, Henkjan Honing embarks upon the quest to discover the cognitive and biological mechanisms that underpin musicality."

Hooked on music: What makes music catchy?

Presentation of hooked-game at the Science Museum in August 2014.

Everyone knows a hook when they hear one, but scientists don’t know why. By playing the Hooked on Music game you are exploring the science of songs and helping us to unlock what makes music catchy.

#HookedonMusic is a citizen science experiment involving the Manchester Science festival, produced by the Museum of Science & Industry in association with the University of Amsterdam. In devising an online game for all to enjoy, we try to harness the wisdom of the crowd to understand and quantify the effect of catchiness on musical memory. Explore the game here.

Presentation of hooked-game at the Science Museum in August 2014.

For more information on #HookedonMusic see the About on www.hookedonmusic.org.uk.
For more online experiments see MCG website.

J.A. Burgoyne, D. Bountouridis, J. van Balen, & H. Honing (2013). Hooked: A Game for Discovering What Makes Music Catchy. Proceedings of the 14th International Society for Music Information Retrieval Conference, 245-250. Curitiba, Brazil.

Interested in music gaming and software development?

We are looking for a researcher with experience in iOS development who can help us explore the question of 'what makes music catchy?' with an innovative music quiz game. For more information and detailed instructions on how to apply see here. Deadline for applications is 15 August 2013.

Burgoyne, J. A., Bountouridis. D., Balen, J. van, & Honing, H. (in press). Hooked: A game for discovering what makes music catchy. Proceedings of ISMIR. Curitiba, Brasil.

Can the domains of Music Cognition and Music Information Retrieval inform each other?

In about a weeks time the 13th ISMIR (International Society for Music Information Retrieval) conference will be held. This is a conference on the processing, searching, organizing and accessing music-related data. It attracts a research community that is intrigued by the revolution in music distribution and storage brought about by digital technology which generated quite some research activity and interest in academia as well as in industry.

In this discipline, referred to as Music Information Retrieval (or MIR for short), the topic is not so much to understand and model music (like in the field of music cognition), but to design robust and effective methods to locate and retrieve musical information, including tasks like query-by-humming, music recommendation, music recognition, and genre classification.

A common approach in MIR research is to use information-theoretic models to extract information from the musical data, be it the audio recording itself or all kinds of meta-data, such as artist or genre classification. With advanced machine learning techniques, and the availability of so-called ‘ground truth’ data (i.e., annotations made by experts that the algorithm uses to decide on the relevance of the results for a certain query), a model of retrieving relevant musical information is constructed. Overall, this approach is based on the assumption that all relevant information is present in the data and that it can, in principle, be extracted from that data (data-oriented approach).

Several alternatives have been proposed, such as models based on perception-based signal processing or mimetic and gesture-based queries. However, with regard to the cognitive aspects of MIR (the perspective of the listener), some information might be implicit or not present at all in the data. Especially in the design of similarity measures (e.g., ‘search for songs that sound like X’) it becomes clear quite quickly that not all required information is present in the data. Elaborating state-of-the-art MIR techniques with recent findings from music cognition seems therefore a natural next step in improving (exploratory) search engines for music and audio (cognition-based approach) (cf. Honing, 2010).

A creative paper, discussing the differences and overlaps between the two fields in dialog form, is about to appear in the proceedings of the upcoming ISMIR conference. Emanuel Bigand –a well-known music cognition researcher–, and Jean-Julien Aucouturier –MIR researcher–, wrote a fictitious dialog:

“Mel is a MIR researcher (the audio type) who's always been convinced that his field of research had something to contribute to the study of music cognition. His feeling, however, hasn't been much shared by the reviewers of the many psychology journals he tried submitting his views to. Their critics, rejecting his data as irrelevant, have frustrated him - the more he tried to rebut, the more defensive both sides of the debate became. He was close to give up his hopes of interdisciplinary dialog when, in one final and desperate rejection letter, he sensed an unusual touch of interest in the editor's response. She, a cognitive psychologist named Ann, was clearly open to discussion. This was the opportunity that Mel had always hoped for: clarifying what psychologists really think of audio MIR, correcting misconceptions that he himself made about cognition, and maybe, developing a vision of how both fields could work together. The following is the imaginary dialog that ensued. Meet Dr Mel Cepstrum, the MIR researcher, and Prof. Ann Ova, the psychologist.”

Aucouturier, J., & Bigand, E. (2012). Mel Cepstrum & Ann Ova: The Difficult Dialog Between MIR and Music Cognition. Proceedings of the 13th International Society for Music Information Retrieval Conference, 397-402.

Honing, H. (2010). Lure(d) into listening: The potential of cognition-based music information retrieval. Empirical Musicology Review, 5(4), 121-126.  

Volk. A., & Honingh, A. (eds) (2012). Special Issue: Mathematical and Computational Approaches to Music: Three Methodological Reflections Journal of Mathematics and Music, 6 (2). 10.1080/17459737.2012.704154