Collaborative audio enhancement using probabilistic latent component sharing

Minje Kim, Paris Smaragdis

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

Abstract

This paper presents a collaborative audio enhancement system that aims to recover common audio sources from multiple recordings of a given audio scene. We do so in the context where each recording is uniquely corrupted. To this end, we propose a method of simultaneous probabilistic latent component analyses on synchronized inputs. In the proposed model, some of the parameters are fixed to be same during and after the learning process to capture common audio content while the rest models unwanted recording-specific interferences and artifacts. Our model also allows for prior knowledge about the parameters of the model, e.g. representative spectra of the components, to be incorporated in the factorization. A post processing scheme that consolidates the extracted sources from the set of inputs is also proposed to handle the possible loss of certain frequency regions. Experiments on commercial music signals with various artifacts show the merit of the proposed method.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages896-900
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

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

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

Keywords

  • Convolutive Common Nonnegative Matrix Factorization
  • Crowdsourcing
  • Nonnegative Matrix Partial Co-Factorization
  • Probabilistic Latent Component Analysis

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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