SINGLE-CHANNEL SPEAKER-DEPENDENT SPEECH ENHANCEMENT EXPLOITING GENERIC NOISE MODEL LEARNED BY NON-NEGATIVE MATRIX FACTORIZATION - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
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SINGLE-CHANNEL SPEAKER-DEPENDENT SPEECH ENHANCEMENT EXPLOITING GENERIC NOISE MODEL LEARNED BY NON-NEGATIVE MATRIX FACTORIZATION

Abstract

This paper considers the single-channel speech separation problem given a noisy observation recorded by a microphone. More precisely, we focus on the speaker-dependent approach where spectral characteristic of target speech is learned in advance from a clean example. In training process, we propose to learn a generic spectral model for noise source by collecting various types of environmental noise via the established non-negative matrix factorization framework. In speech enhancement process, we propose to combine two existing group sparsity-inducing penalties in the optimization function and derive the corresponding algorithm for parameter estimation based on multiplicative update (MU) rule. Experiment result over mixtures containing different real-world noises confirms the effectiveness of our approach.
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Dates and versions

hal-01288277 , version 1 (15-03-2016)

Identifiers

  • HAL Id : hal-01288277 , version 1

Cite

Hien-Thanh Duong, Quoc-Cuong Nguyen, Cong-Phuong Nguyen, Ngoc Q. K. Duong. SINGLE-CHANNEL SPEAKER-DEPENDENT SPEECH ENHANCEMENT EXPLOITING GENERIC NOISE MODEL LEARNED BY NON-NEGATIVE MATRIX FACTORIZATION. IEEE International Conference on Electronics, Information and Communication, Jan 2016, Da Nang, Vietnam. ⟨hal-01288277⟩

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