Speech Denoising Using Non-negative Matrix Factorization with Kullback-Leibler Divergence and Sparseness Constraints

Jimmy Ludeña-Choez; Ascensión Gallardo-Antolín

doi:10.1007/978-3-642-35292-8_22

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Speech Denoising Using Non-negative Matrix Factorization with Kullback-Leibler Divergence and Sparseness Constraints

Jimmy Ludeña-Choez Ascensión Gallardo-Antolín

Year: 2012 Communications in computer and information science Pages: 207-216 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-642-35292-8_22

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Keywords:

Non-negative matrix factorization Divergence (linguistics) Computer science Kullback–Leibler divergence Speech recognition Euclidean distance Matrix decomposition Noise reduction Speech enhancement Noise (video) A priori and a posteriori Matrix (chemical analysis) Pattern recognition (psychology) Factorization Artificial intelligence Algorithm

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Speech and Audio Processing

Physical Sciences → Computer Science → Signal Processing

Speech Recognition and Synthesis

Physical Sciences → Computer Science → Artificial Intelligence

Blind Source Separation Techniques

Physical Sciences → Computer Science → Signal Processing

Speech Denoising Using Non-negative Matrix Factorization with Kullback-Leibler Divergence and Sparseness Constraints

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