Journal
IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
Volume 18, Issue 3, Pages 602-612Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TASL.2009.2036306
Keywords
Automatic segmentation; dynamic texture model (DTM); music modeling; music similarity
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Funding
- National Science Foundation (NSF) [IGERT DGE-0333451]
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We consider representing a short temporal fragment of musical audio as a dynamic texture, a model of both the timbral and rhythmical qualities of sound, two of the important aspects required for automatic music analysis. The dynamic texture model treats a sequence of audio feature vectors as a sample from a linear dynamical system. We apply this new representation to the task of automatic song segmentation. In particular, we cluster audio fragments, extracted from a song, as samples from a dynamic texture mixture (DTM) model. We show that the DTM model can both accurately cluster coherent segments in music and detect transition boundaries. Moreover, the generative character of the proposed model of music makes it amenable for a wide range of applications besides segmentation. As examples, we use DTM models of songs to suggest possible improvements in other music information retrieval applications such as music annotation and similarity.
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