92,703 research outputs found
Music analysis by computer:ontology and epistemology
This chapter examines questions of what is to be analysed in computational music analysis, what is to be produced, and how one can have confidence in the results. These are not new issues for music analysis, but their consequences are here considered explicitly from the perspective of computational analysis. Music analysis without computers is able to operate with multiple or even indistinct conceptions of the material to be analysed because it can use multiple references whose meanings shift from context to context. Computational analysis, by contrast, must operate with definite inputs and produce definite outputs. Computational analysts must therefore face the issues of error and approximation explicitly. While computational analysis must retain contact with the music analysis as it is generally practised, I argue that the most promising approach for the development of computational analysis is not systems to mimic human analysis, but instead systems to answer specific music-analytical questions. The chapter concludes with several consequent recommendations for future directions in computational music analysis
Maths, Computation and Flamenco: overview and challenges
Flamenco is a rich performance-oriented art music genre from Southern Spain
which attracts a growing community of aficionados around the globe. Due to its
improvisational and expressive nature, its unique musical characteristics, and
the fact that the genre is largely undocumented, flamenco poses a number of
interesting mathematical and computational challenges. Most existing approaches
in Musical Information Retrieval (MIR) were developed in the context of popular
or classical music and do often not generalize well to non-Western music
traditions, in particular when the underlying music theoretical assumptions do
not hold for these genres. Over the recent decade, a number of computational
problems related to the automatic analysis of flamenco music have been defined
and several methods addressing a variety of musical aspects have been proposed.
This paper provides an overview of the challenges which arise in the context of
computational analysis of flamenco music and outlines an overview of existing
approaches
Proceedings of the 6th International Workshop on Folk Music Analysis, 15-17 June, 2016
The Folk Music Analysis Workshop brings together computational music analysis and ethnomusicology. Both symbolic and audio representations of music are considered, with a broad range of scientific approaches being applied (signal processing, graph theory, deep learning). The workshop features a range of interesting talks from international researchers in areas such as Indian classical music, Iranian singing, Ottoman-Turkish Makam music scores, Flamenco singing, Irish traditional music, Georgian traditional music and Dutch folk songs. Invited guest speakers were Anja Volk, Utrecht University and Peter Browne, Technological University Dublin
Automatic estimation of harmonic tension by distributed representation of chords
The buildup and release of a sense of tension is one of the most essential
aspects of the process of listening to music. A veridical computational model
of perceived musical tension would be an important ingredient for many music
informatics applications. The present paper presents a new approach to
modelling harmonic tension based on a distributed representation of chords. The
starting hypothesis is that harmonic tension as perceived by human listeners is
related, among other things, to the expectedness of harmonic units (chords) in
their local harmonic context. We train a word2vec-type neural network to learn
a vector space that captures contextual similarity and expectedness, and define
a quantitative measure of harmonic tension on top of this. To assess the
veridicality of the model, we compare its outputs on a number of well-defined
chord classes and cadential contexts to results from pertinent empirical
studies in music psychology. Statistical analysis shows that the model's
predictions conform very well with empirical evidence obtained from human
listeners.Comment: 12 pages, 4 figures. To appear in Proceedings of the 13th
International Symposium on Computer Music Multidisciplinary Research (CMMR),
Porto, Portuga
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