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)
Number of pages4
StatePublished - 2004
Event8th International Conference on Spoken Language Processing, ICSLP 2004 - Jeju, Jeju Island, Korea, Republic of
Duration: Oct 4 2004Oct 8 2004


Other8th International Conference on Spoken Language Processing, ICSLP 2004
Country/TerritoryKorea, Republic of
CityJeju, Jeju Island

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language


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