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Selflearning Codebook Speech Enhancement

Authors:
Heese, F.Nelke, C. M.Niermann, M.Vary, P.
Book Title:
ITG-Fachtagung Sprachkommunikation
Publisher:
VDE Verlag GmbH
Date:
Sep. 2014
Language:
English

Abstract

A novel speech enhancement system is presented which exploits a codebook for noise estimation. In contrast to state-of-the-art noise estimators which usually rely on the assumption that the noise signal is only slightly time-varying, codebook approaches allow also non-stationary environments. The basic concept of the proposed codebook noise estimation is a superposition of a scaled speech and noise codebook entry. In order to be independent of a priori noise knowledge, the new estimator is able to learn new noise types online. Training vectors for codebook updates are identified using a speech activity detector (VAD) and a codebook mismatch measure. The VAD is realized as part of the codebook matching. A Wiener filter or any state-of-the-art weighting rule can be applied subsequently for speech enhancement. Experiments confirmed that the new system is able to learn new noise types and provides consistent performance.

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