On the effectiveness of PARAFAC-based estimation for blind speech separation

Kleanthis N. Mokios, Alexandros Potarnianos, Nicholas D. Sidiropoulos

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

4 Scopus citations

Abstract

This work establishes the effectiveness of parallel factor (PARAFAC) analysis in blind speech separation (BSS) problems. The BSS problem is formulated as a conjugate-symmetric PARAFAC model that is fitted optimally, using an efficient alternating least-squares algorithm that converges monotonically. The identifiability properties of the model are also presented, revealing the much broader identifiability potential of joint-diagonalization- based BSS methods. In order to focus on estimation performance, perfect resolution of the permutation ambiguity is assumed. Simulations under varying reverberation conditions and comparison with previous estimation methods that are widely used in BSS problems demonstrate significant performance gains. Signal-to-interference (SIR) ratio improvement of over 27 dB is achieved using PARAFAC. Average SIR gains of 2.5 and 6.3 dB are achieved compared to state-of-the-art FastICA[2] and FDSOS (Parra's)[5] estimation algorithms, respectively.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages153-156
Number of pages4
DOIs
StatePublished - Sep 16 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: Mar 31 2008Apr 4 2008

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
CountryUnited States
CityLas Vegas, NV
Period3/31/084/4/08

Keywords

  • Blind speech separation
  • Estimation method
  • Non-stationary signals
  • Parallel factor analysis

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