Efficient neighborhood-based topic modeling for collaborative audio enhancement on massive crowdsourced recordings

Minje Kim, Paris Smaragdis

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

Abstract

Collaborative Audio Enhancement (CAE) aims at separating a dominant source from crowdsourced recordings of a scene. This paper proposes a CAE setup as a big ad-hoc microphone array problem, assuming hundreds of sensors scattered over a large scene, e.g. a concert hall or a street riot. An important characteristic in such cases is the fact that not all sensors capture useful information, mainly because of the existence of strong local noise interferences and recording artifacts. This renders traditional array processing techniques inadequate for tasks such as source enhancement. One way to recover the most common source while suppressing recording-specific interference, is to share latent components across simultaneous models on multiple magnitude spectrograms. The proposed method improves on the quality and the computational requirements of such a model by using a two-stage nearest-neighborhood search at every EM update. Its optional first-round search uses Hamming distance between hashed spectrograms to quickly find a redundant candidate set, and then a subsequent step narrows the set down to a subset using more appropriate cross entropy. Experimental results show that the proposed neighborhood schemes converge to the better quality solutions faster than the comprehensive model using all data.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages41-45
Number of pages5
ISBN (Electronic)9781479999880
DOIs
StatePublished - May 18 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: Mar 20 2016Mar 25 2016

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2016-May
ISSN (Print)1520-6149

Other

Other41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
CountryChina
CityShanghai
Period3/20/163/25/16

Keywords

  • Ad-hoc Microphone Array
  • Collaborative Audio Enhancement
  • Probabilistic Latent Component Sharing
  • Probabilistic Topic Models
  • Social Data

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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