Efficient manifold preserving audio source separation using locality sensitive hashing

Minje Kim, Paris Smaragdis, Gautham J. Mysore

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

Abstract

We propose an efficient technique to learn probabilistic hierarchical topic models that are designed to preserve the manifold structure of audio data. The consideration of the data manifold is important, as it has been shown to provide superior performance in certain audio applications such as source separation. However, the high computational cost of a sparse encoding step due to the requirement of a large dictionary prevents it from being used in real-world applications such as real-time speech enhancement and the analysis of big audio data. In order to achieve a substantial speed-up of this step, while still respecting the data manifold, we propose to harmonize a particular type of locality sensitive hashing with the hierarchical topic model. The proposed use of hashing can reduce the computational complexity of the sparse encoding by providing candidates of non-zero activations, where the candidate set is built based on Hamming distance. The hashing step is followed by comprehensive sparse coding that considers those candidates only, rather than the entire dictionary. Experimental results show that the proposed hashing technique can provide audio source separation results comparable to the similar system without hashing, but with significantly less and cheaper computation.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages479-483
Number of pages5
ISBN (Electronic)9781467369978
DOIs
StatePublished - Aug 4 2015
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: Apr 19 2014Apr 24 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2015-August
ISSN (Print)1520-6149

Other

Other40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
CountryAustralia
CityBrisbane
Period4/19/144/24/14

Keywords

  • Locality Sensitive Hashing
  • Source Separation
  • Topic Modeling
  • Winner Take All Hashing

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Kim, M., Smaragdis, P., & Mysore, G. J. (2015). Efficient manifold preserving audio source separation using locality sensitive hashing. In 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings (pp. 479-483). [7178015] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2015-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2015.7178015