Dynamic Bayesian Networks for Symbolic Polyphonic Pitch Modeling - Publikacja - MOST Wiedzy

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Dynamic Bayesian Networks for Symbolic Polyphonic Pitch Modeling

Abstrakt

Symbolic pitch modeling is a way of incorporating knowledge about relations between pitches into the process of an- alyzing musical information or signals. In this paper, we propose a family of probabilistic symbolic polyphonic pitch models, which account for both the “horizontal” and the “vertical” pitch struc- ture. These models are formulated as linear or log-linear interpo- lations of up to fi ve sub-models, each of which is responsible for modeling a different type of relation. The ability of the models to predict symbolic pitch data is evaluated in terms of their cross-en- tropy, and of a newly proposed “contextual cross-entropy” mea- sure. Their performance is then m easuredonsynthesizedpoly- phonic audio signals in terms of the accuracy of multiple pitch estimation in combination with a Nonnegative Matrix Factoriza- tion-based acoustic model. In both experiments, the log-linear com- bination of at least one “vertical” (e.g., harmony) and one “hori- zontal” (e.g., note duration) sub-model outperformed a pitch-de- pendent Bernoulli prior by more than 60% in relative cross-en- tropy and 3% in absolute multiple pitch estimation accuracy. This work provides a proof of concept of the usefulness of model inter- polation, which may be used for improved symbolic modeling of other aspects of music in the future.

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:
IEEE Transactions on Audio Speech and Language Processing nr 21, strony 1830 - 1840,
ISSN: 1558-7916
Język:
angielski
Rok wydania:
2013
Opis bibliograficzny:
Raczyński S., Vincent E., Sagayama S.: Dynamic Bayesian Networks for Symbolic Polyphonic Pitch Modeling// IEEE Transactions on Audio Speech and Language Processing. -Vol. 21, nr. 9 (2013), s.1830-1840
Weryfikacja:
Politechnika Gdańska

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