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

Akira Maezawa, Hiroshi G. Okuno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5212-5216
Number of pages5
ISBN (Print)9781479928927
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence
Duration: 2014 May 42014 May 9

Other

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

Keywords

  • Audio-audio alignment
  • Nonparametric hierarchical bayes

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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