Abstract

Our goal is to study the statistical methods for source separation based on temporal and frequency specific features by using particle filtering. Particle filtering is an advanced state-space Bayesian estimation technique that supports non-Gaussian and nonlinear models along with time-varying noise, allowing for a more accurate model of the underlying system dynamics. We present a system that combines standard speech processing techniques in a novel method to separate two noisy speech sources. The system models the pitch and amplitude over time separately, and adopts particle filtering to reduce complexity by generating a discrete distribution that approximates well the desired continuous distribution. Preliminary results that demonstrate the separation of two noisy sources using this system are presented.

Original languageEnglish (US)
Pages2673-2676
Number of pages4
StatePublished - Jan 1 2004
Event8th International Conference on Spoken Language Processing, ICSLP 2004 - Jeju, Jeju Island, Korea, Republic of
Duration: Oct 4 2004Oct 8 2004

Other

Other8th International Conference on Spoken Language Processing, ICSLP 2004
CountryKorea, Republic of
CityJeju, Jeju Island
Period10/4/0410/8/04

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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    Gandhi, M. A., & Hasegawa-Johnson, M. A. (2004). Source separation using particle filters. 2673-2676. Paper presented at 8th International Conference on Spoken Language Processing, ICSLP 2004, Jeju, Jeju Island, Korea, Republic of.