Acoustic event recognition using dominant spectral basis vectors

Woohyun Choi, Sangwook Park, David K. Han, Hanseok Ko

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

7 Citations (Scopus)

Abstract

This paper proposes a novel filter bank composed of dominant Spectral Basis Vectors (SBVs) in a spectrogram. Spectral envelopes represented by the SBVs have shown to be excellent characteristic features for discriminating different acoustic events in noisy environment. Non-negative Matrix Factorization (NMF) and non-negative K-SVD (NKSVD) for part-based and holistic representations extract dominant SBVs from a spectrogram. The effectiveness of the proposed method is demonstrated on a database of real life recordings via experiments, and its robust performance is compared to conventional methods.

Original languageEnglish
Title of host publicationProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
PublisherInternational Speech and Communication Association
Pages2002-2006
Number of pages5
Volume2015-January
Publication statusPublished - 2015
Event16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015 - Dresden, Germany
Duration: 2015 Sep 62015 Sep 10

Other

Other16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015
CountryGermany
CityDresden
Period15/9/615/9/10

Keywords

  • Acoustic event recognition
  • Dictionary learning
  • Dominant spectral basis vector
  • K-SVD
  • Non-negative matrix factorization
  • Robust feature extraction
  • Spectral envelope

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

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