Abstract
This article addresses the problem of single-channel speech enhancement in the presence of nonstationary noise. A novel-modified NMF-based filter bank approach is proposed for speech enhancement. The method consists of filter bank analysis of the noisy input signal followed by extraction of speech signal based on a modified NMF (MNMF) by learning a speaker-independent speech dictionary using a precomputed noise dictionary. The proposed method works well with the real-world nonstationary noise independent to the speaker. The method is evaluated using a speech database consists of different speakers showing promising enhancement performance by reducing the nonstationary noise together with improving PESQ (perceptual evaluation of speech quality) compared to other competitive state-of-the-art methods.
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Notes
- 1.
The Noizeus speech corpus is publicly available online at the following url: http://www.utdallas.edu/~loizou/speech/noizeus.
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Sattar, F., Jin, F. (2016). A Modified NMF-Based Filter Bank Approach for Enhancement of Speech Data in Nonstationary Noise. In: Naik, G. (eds) Non-negative Matrix Factorization Techniques. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48331-2_5
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DOI: https://doi.org/10.1007/978-3-662-48331-2_5
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