|
|
NWO-VIDI
project MUSIVA Modelling musical similarity
over time
through the variation
principle
Principal
investigator: Dr. Anja Volk |
|
|
|
News
Hendrik Vincent Koops presented Personalizing Chord Labels though deep learning of shared representations at Systematic Approaches to Deep Learning Methods for Audio, Sept 11-17, 2017, Erwin Schroedinger Institute, Vienna.
Iris Yuping Ren chaired the panel session
Applying music patterns in generation at the 2nd
Conference on Computer Simulation of Musical Creativity,
Milton Keynes, UK, Sep 13, 2017.
Anja Volk gave the public lecture Lekker (algo)ritme at Culturele Zondag Utrecht Centraal on June 5, 2017. Podcast. Blog Studium Generale.
Anja Volk co-led the launch of the Transactions of the International Society for Music Information Retrieval, the open access journal of the ISMIR society at Ubiquity press. She serves as Editor-in-Chief, together with Simon Dixon and Emilia Gomez.
Iris Yuping Ren joined the lab in January
2017. She works on Computational Music Structure Analysis
employing the functional programming paradigm.
Special Issue Music similarity: Concepts, cognition and computation, Guest Editors: Anja Volk, Elaine Chew, Elizabeth Hellmuth Margulis, Christina Anagnostopoulou, Journal of New Music Research, appeared September 2016. Follow up to Lorentz workshop on Music similarity.
Marcelo Rodriguez Lopez successfully defended his PhD Thesis Automatic Melody Segmentation on June 20, 2016.
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
article here.
Project
Description:
Introduction
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.
Significance
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
genre. High-level
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.
Approach
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
similarity.
Research Team
The
research
team includes:
principal investigator: Dr. Anja Volk
Postdoc: Dr. Bas de Haas
PhD student: Marcelo Rodriguez-Lopez
PhD student: Hendrik Vincent Koops
Publications
Ren, I., Koops, H.V., Volk, A., Swierstra, W. (2017), In search of the consensus among musical pattern discovery algorithms, to appear at Proceedings of the 18th International Society for Music Information Retrieval Conference, Suzhou, China.
De Valk, R., Volk, A., Holzapfel, A., Pikrakis, A., Kroher, N. and Six, J. (2017), MIRchiving: Challenges and opportunities of connecting MIR research and digital music archive, accepted for publication in Proceedings of the 4th International workshop on Digital Libraries for Musicology, ACM Digital Library, Shanghai, China.
Van Kranenburg, P., De Bruin, M., Volk, A. (2017). Documenting a song culture: The Dutch Song Database as a resource for musicological research. International Journal on Digital Libraries.
Janssen, B., van Kranenburg, P., Volk, A. (2017). Finding occurrences of melodic segments in folk songs employing symbolic similarity measures, Journal of New Music Research, 46(2), 118-134.
Koops, H.V., de Haas, W.B., Bransen, J. and Volk, A. (2017), Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations, Proceedings of the 1st International Workshop Deep Learning for Music, Anchorage, 19-25.
Odekerken, D., Volk, A., & Koops, H.V. (2017), Rhythmic Patterns in Ragtime and Jazz, Proceedings of the 7th International Workshop on Folk Music Analysis, 44-49, Malaga, Spain.
Boot, P., Volk, A., & de Haas, W.B. (2016). Evaluating the Role of Repeated Patterns in Folk Song Classification and Compression, Journal of New Music Research.
De Haas, W.B. & Volk, A. (2016). Meter Detection in Symbolic Music Using Inner Metric Analysis, Proceedings of the 17th Conference of the International Society for Music Information Retrieval (ISMIR), New York, USA, August 7-11.
Janssen, B., van Kranenburg, P. & Volk, A. (2015). A comparison of symbolic similarity measures for finding occurrences of melodic segments. In: Proceedings of the 16th ISMIR Conference, Malaga, Spain, October 26-30, (pp. 659-665). Malaga, Spain: ISMIR press.
Koops, H. V., Volk, A. & de Haas, W.B. (2015). Corpus-Based Rhythmic Pattern Analysis of Ragtime Syncopation. In Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR(pp. 483-489) (7 p.). Malaga, Spain: ISMIR press.
Rodriguez Lopez, M.E. & Volk, A. (2015). Location Constraints for Repetition-Based Segmentation of Melodies. Proceedings of the 5th International Conference in Mathematics and Computation in Music (MCM) (pp. 73-84) (12 p.). Springer.
Rodriguez Lopez, M.E., Bountouridis, D. & Volk, A. (2015). Novel Music Segmentation Interface and the Jazz Tune Collection. Proceedings of the 5th International Workshop on Folk Music Analysis (pp. 99-105) (7 p.). Paris: CNRS.
Rodriguez Lopez, M.E. & Volk, A. (2015). On the Evaluation of Automatic Segment Boundary Detection. Music, Mind, Embodiment - Proceedings of the 11th International Symposium on Computer Music Multidisciplinary Research (CMMR) (pp. 234-246) (14 p.). Plymouth: LMA publications.
Rodriguez Lopez, M.E. & Volk, A. (2015). Selective Acquisition Techniques for Enculturation-Based Melodic Phrase Segmentation. Proceedings of the 16th Conference of the International Society for Music Information Retrieval (ISMIR) (pp. 218-224) (7 p.). Malaga: ISMIR press.
Rodriguez Lopez, M.E., de Haas, Bas & Volk, A. (2014). Comparing repetition-based melody segmentation models. Proceedings of the 9th Conference on Interdisciplinary Musicology (CIM14) (pp. 143-148) (6 p.). Berlin: SIMPK and ICCMR.
Rodriguez Lopez, M.E., Volk, A. & Bountouridis, D. (2014). Multi-strategy Segmentation of Melodies. Proceedings of the 15th Conference of the International Society for Music Information Retrieval (ISMIR 2014) (pp. 207-212) (6 p.). Taipei: ISMIR press.
Rodriguez Lopez, M.E. & Volk, A. (2014). Symbolic Segmentation: A Corpus-Based Analysis of Melodic Phrases. Sound, Music, and Motion - Post Proceedings of the 10th international symposium, CMMR 2013 (pp. 548-557) (10 p.). Cham: Springer.
Janssen, B., De Haas, W.B., Volk, A., & Van Kranenburg, P. (2013). Discovering repeated patterns in music: state of knowledge, challenges, perspectives, Proceedings of the 10th International Symposium on Computer Music Modeling and Retrieval (CMMR 2013), CNRS - Laboratoire de Mecanique et d'Acoustique, Marseille, France.
Van Kranenburg, P., Volk, A., & Wiering, F. (2013). A Comparison between Global and Local Features for Computational Classification of Folk Song Melodies, Journal of New Music Research, Vol. 42 (1), 1-18.
De Haas, W.B., Volk, A. & Wiering, F. (2013). Structural segmentation of music based on repeated harmonies. Proceedings of the International Symposium on Multimedia (pp. 255-258) (4 p.). Anaheim: IEEE.
Rodriguez-Lopez, M.E. & Volk, A. (2013). Symbolic Segmentation: A Corpus-Based Analysis of Melodic Phrases, Proceedings of the 10th International Symposium on Computer Music Modeling and Retrieval (CMMR 2013), CNRS - Laboratoire de Mecanique et d'Acoustique, Marseille, France.
Volk, A. & De Haas, W.B. (2013). A Corpus-based study on Ragtime syncopation. Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR), 2013, Curitiba, Brazil.
Volk, A. & van Kranenburg, P. (2012), Melodic similarity among folk songs: An annotation study on similarity-based categorization in music, Musicae Scientiae, 16(3), 317-339.
Volk, A. & Honingh, A., (2012). Mathematical and computational approaches to music: challenges in an interdisciplinary enterprise. Journal of Mathematics and Music, Vol. 6, No. 2, pp. 73-81.
Rodriguez Lopez,
M.E. &
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,
2011.
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.
Contact for more detailed project description:
Anja Volk
Department of Information and Computing Sciences
Utrecht University
email and further contact
details