Voice Activity Detection Via Noise Reducing Using Non-Negative Sparse Coding

This letter presents a voice activity detection (VAD) approach using non-negative sparse coding to improve the detection performance in low signal-to-noise ratio (SNR) conditions. The basic idea is to use features extracted from a noise-reduced representation of original audio signals. We decompose...

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Vydáno v:IEEE signal processing letters Ročník 20; číslo 5; s. 475 - 478
Hlavní autoři: Teng, Peng, Jia, Yunde
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.05.2013
Témata:
Conditional random fields
Dictionaries
Encoding
Feature extraction
noise reducing
non-negative sparse coding
Signal to noise ratio
Speech
Vectors
voice activity detection
ISSN:1070-9908, 1558-2361
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Shrnutí:This letter presents a voice activity detection (VAD) approach using non-negative sparse coding to improve the detection performance in low signal-to-noise ratio (SNR) conditions. The basic idea is to use features extracted from a noise-reduced representation of original audio signals. We decompose the magnitude spectrum of an audio signal on a speech dictionary learned from clean speech and a noise dictionary learned from noise samples. Only coefficients corresponding to the speech dictionary are considered and used as the noise-reduced representation of the signal for feature extraction. A conditional random field (CRF) is used to model the correlation between feature sequences and voice activity labels along audio signals. Then, we assign the voice activity labels for a given audio by decoding the CRF. Experimental results demonstrate that our VAD approach has a good performance in low SNR conditions.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2013.2252615