Hanwook Chung

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In this letter, we introduce a discriminative training algorithm of the basis vectors in the nonnegative matrix factorization (NMF) model for single-channel speech enhancement. The basis vectors for the clean speech and noises are estimated simultaneously during the training stage by incorporating the concept of classification from machine learning.(More)
In this paper, we introduce a single channel speech enhancement algorithm based on regularized non-negative matrix factorization (NMF). In our proposed formulation, the log-likelihood function (LLF) of the magnitude spectral components, based on Gaussian mixture models (GMM) for both the speech and background noise signals, is included as a regularization(More)
In this paper, we propose a basis compensation algorithm for non-negative matrix factorization (NMF) models as applied to supervised single-channel speech enhancement. In the proposed framework, we use extra free basis vectors for both the clean speech and noise during the enhancement stage in order to capture the features which are not included in the(More)
In this paper, we introduce a discriminative training algorithm of the non-negative matrix factorization (NMF) model for single-channel enhancement of convolutive noisy speech. The basis vectors for the clean speech and noises are estimated simultaneously during the training stage by incorporating the concept of classification from machine learning.(More)
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