A statistical model-based residual echo suppression

Seung Yeol Lee, Nam Soo Kim

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

In this letter, we propose a novel residual echo suppression (RES) algorithm based on a statistical model constructed in the acoustic echo cancellation framework. In the proposed approach, all the possible near-end and far-end signal conditions are classified into four distinct hypotheses, and the power spectral density estimation is carried out according to the result of hypothesis testing. The distribution of each signal component is characterized by a parametric model, and the conventional likelihood ratio test is performed to make an optimal decision. The experimental results show that the proposed algorithm yields improved performance compared to that of the previous RES technique.

Original languageEnglish
Pages (from-to)758-761
Number of pages4
JournalIEEE Signal Processing Letters
Volume14
Issue number10
DOIs
StatePublished - Oct 2007

Keywords

  • Acoustic echo cancellation
  • Post filter
  • Residual echo suppression (RES)

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