Publications – Details
Selflearning Codebook Speech Enhancement
- Authors:
- Florian Heese, Christoph Matthias Nelke, Markus Niermann, and Peter Vary
- Book Title:
- ITG-Fachtagung Sprachkommunikation
- Event Date:
- 24.-26.9.2014
- Publisher:
- VDE Verlag GmbH
- Date:
- Sept. 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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