Audio part mixture alignment based on hierarchical nonparametric Bayesian model of musical audio sequence collection

Akira Maezawa, Hiroshi G. Okuno

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

This paper proposes 'audio part mixture alignment,' a method for temporally aligning multiple audio signals, each of which is a rendition of a non-disjoint subset of a common piece of music. The method decomposes each audio signal into shared components and components unique to each rendition. At the same time, it aligns each audio signal based on the shared component. Decomposition of audio signal is modeled using a hierarchical Dirichlet process (Hierarchical DP, HDP), and sequence alignment is modeled as a left-to-right hidden Markov model (HMM). Variational Bayesian inference is used to jointly infer the alignment and component decomposition. The proposed method is compared with a classic audio-to-audio alignment method, and it is found that the proposed method is more robust to the discrepancy of parts between two audio signals.

本文言語English
ホスト出版物のタイトルICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ5212-5216
ページ数5
ISBN(印刷版)9781479928927
DOI
出版ステータスPublished - 2014
外部発表はい
イベント2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence
継続期間: 2014 5月 42014 5月 9

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CityFlorence
Period14/5/414/5/9

ASJC Scopus subject areas

  • 信号処理
  • ソフトウェア
  • 電子工学および電気工学

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