NWO-VIDI project MUSIVA
Modelling musical similarity over time
through the variation principle
Principal investigator: Dr. Anja Volk
Special Issue Music similarity: Concepts, cognition and computation, Guest Editors: Anja Volk, Elaine Chew, Elizabeth Hellmuth Margulis, Christina Anagnostopoulou, Journal of New Music Research, to appear September 2016. Follow up to Lorentz workshop on Music similarity.
Peter Boot received the runner
up award of the Graduate School of Natural
Sciences for his master thesis Using
Discovered and Annotated Patterns as Compression
Method for determining Similarity between Folk
Songs. Download the corresponding journal
The aim of this project is to deliver
cognition-based computational models on music similarity that
ground in the variation principle employed in
classical, folk and popular music. The project integrates
knowledge and methods from Music Information Retrieval,
Musicology and Cognitive Science.
The assessment of similarity is fundamental for cognitive processes. Cognitive Science provides formal models for the theoretical framework and empirical measurement of similarity, demonstrating its crucial influence on our conception of the world. However, no comprehensive theory exists on how listeners use similarity to predict, categorize or appreciate music. This is a major problem in the rapidly growing discipline of Music Information Retrieval (MIR). MIR researches methods that allow users to retrieve music that is similar to musical queries representing their needs via the Internet. Developing content-based search methods for music faces the challenge of relating features of the music to listeners' experience of similarity. Most methods developed in MIR extract low-level features from the music, severely limiting the cognitive plausibility of search results and hence impairing the usefulness of current retrieval systems.
This project investigates the fundamental principle of variation in music studied in Musicology and Cognitive Science as a means to establish similarity. The project researches computational modelling of music similarity in the symbolic domain (i.e. using perception-related notation, such as MIDI or **kern) based on the variation of structural elements (such as motives, rhythms and chord sequences). Specifically, it takes into account the interaction between global and local features of the music.
Modelling similarity based on the variation principle is urgently needed to establish a general theory of similarity in music. This is crucial for the successful design of MIR systems and opens up new research lines in music cognition research. Integrating the concept of variation (Musicology), formal models of similarity (Cognitive Science) and computational modelling (Information Retrieval) into a comprehensive approach to music similarity delivers important aspects for a general theory on similarity across different domains.
Music Information Retrieval:
The largest part of modelling
music similarity in Music Information Retrieval is currently in
the audio domain. The extracted low-level features
allow the comparison of broad categories of music, such as
genres, but are less successful for establishing similarity for
finer-grained data, such as for similar songs within the same
features extracted in the symbolic domain from formats
such as MIDI or **kern are closer to the way people perceive
music. This project addresses the high-level processing in
establishing similarity in music which delivers an essential
requirement for building cognitively plausible search algorithms
in Music Information Retrieval. The lack of established concepts
of similarity in music causes the lack of suitable ground truths on which
algorithms can be tested (Downie et
al, 2009). For the three selected music styles, test cases based
on the variation principle are established as a ground truth for
the computational modelling.
Musicology: The musicological discourse on the variation principle is based on small numbers of selected musical examples and has not led to a general concept of variation. The computational modelling allows to formalize the concept and to explicitly test it on a large collection of music.
Cognitive Science: While research in music cognition has strongly focussed on the experienced listener of Western classical music (Peretz, 2006), research on music similarity contributes an excellent topic on basic music skills. Similarity is used as a default method to reason about a domain, even if we do not have specific knowledge about it (Goldstone & Son, 2005). Thus, understanding music similarity demonstrates that accessing music is not reserved to the highly trained specialist. Moreover, models of music similarity contribute an underrepresented domain to similarity research in Cognitive Science and hence contribute new aspects to the search for general principles of similarity across different domains.
Musicology provides analytic descriptions of
music closely related to the phenomenon of similarity that are
based on the variation principle. The term variation
principle refers to the variation of musical patterns
such that listeners are able to relate different components of
a piece of music, or entire pieces, to each other and hence
experience similarity relations. Although no formalized
concept of the variation principle has been developed, studies
on techniques to vary musical patterns such that they
establish similarity in classical, folk and popular music
strongly suggest that the variation principle is a universal
principle in music. Recent studies in Cognitive Science
show that similarity relations based on the variation of
characteristic elements as described in Musicology are
recognized both by experts and novices. This project takes
into account different ways of interaction between global
and local features of the music as a basis for
modelling the concrete
manifestations of the variation principle studied in
Musicology and Cognitive Science. The data-rich approach of
computational modelling within Music
Information Retrieval allows to corroborate the
generality of the variation principle in establishing
The research team includes:
principal investigator: Dr. Anja Volk
Postdoc: Dr. Bas de Haas
PhD student: Hendrik Vincent Koops
Volk, A. (2012). Melodic
Segmentation Using the Jensen-Shannon Divergence,
Proceedings of the 11th International Conference on Machine
Learning and Applications, Boca Raton, USA.
Volk, A., De Haas, W.B. & Van Kranenburg, P. (2012). Towards Modeling Variation in Music as a Foundation of Similarity, Proceedings of the 12th International Conference on Music Perception and Cognition, Thessaloniki, Greece.
Van Kranenburg, P., Volk, A., & Wiering, F. (2012). On Identifying Folk Song Melodies Employing Recurring Motifs, Proceedings of the 12th International Conference on Music Perception and Cognition, Thessaloniki, Greece.
Biro, D., Van Kranenburg, P., Ness, S. G., Tzanetakis, G., & Volk, A. (2012). Stability and Variation in Cadence Formulas in Oral and Semi-Oral Chant Traditions - a Computational Approach, Proceedings of the 12th International Conference on Music Perception and Cognition, Thessaloniki, Greece, 2012.
Volk, A., Wiering, F. & Van Kranenburg, P. (2011). Unfolding
the Potential of Computational Musicology. Proceedings of
the13th International Conference on Informatics and Semiotics in
Organisations (ICISO), Leeuwarden, the Netherlands, 137-144,
Van Kranenburg, P., Wiering, F., & Volk, A. (2011). On Deconstructing the musicological concept of tune family for computational modeling. Proceedings of the Supporting Digital Humanities conference, Kopenhagen.
Department of Information and Computing Sciences
email and further contact details