Online sound structure analysis based on generative model of acoustic feature sequences

Keisuke Imoto, Nobutaka Ono, Masahiro Niitsuma, Yoichi Yamashita

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

Abstract

We propose a method for the online sound structure analysis based on a Bayesian generative model of acoustic feature sequences, with which the hierarchical generative process of the sound clip, acoustic topic, acoustic word, and acoustic feature is assumed. In this model, it is assumed that sound clips are organized based on the combination of latent acoustic topics, and each acoustic topic is represented by a Gaussian mixture model (GMM) over an acoustic feature space, where the components of the GMM correspond to acoustic words. Since the conventional batch algorithm for learning this model requires a huge amount of calculation, it is difficult to analyze the massive amount of sound data. Moreover, the batch algorithm does not allow us to analyze the sequentially obtained data. Our variational Bayes-based online algorithm for this generative model can analyze the structure of sounds sound clip by sound clip. The experimental results show that the proposed online algorithm can reduce the calculation cost by about 90% and estimate the posterior distributions as efficiently as the conventional batch algorithm.

Original languageEnglish
Title of host publicationProceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1316-1321
Number of pages6
ISBN (Electronic)9781538615423
DOIs
Publication statusPublished - 2018 Feb 5
Externally publishedYes
Event9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 - Kuala Lumpur, Malaysia
Duration: 2017 Dec 122017 Dec 15

Publication series

NameProceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Volume2018-February

Other

Other9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Country/TerritoryMalaysia
CityKuala Lumpur
Period17/12/1217/12/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Information Systems
  • Signal Processing

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