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- W2890828644 abstract "In this paper, we present a novel approach for speech enhancement based on nonnegative matrix factorization (NMF) with the speech magnitude spectrum constrained by a codebook. First, we utilize a codebook to model the magnitude spectrum of clean speech and a speech magnitude spectrum codebook is trained containing the priori information of speech. Second, a classic noise estimation algorithm is employed to estimate the power spectral density (PSD) of noise to avoid noise classification. Then, we obtain the basis matrix of the noisy speech by combining the noise spectral with the optimal entry from the speech codebook. The magnitude spectrum of the noisy speech is decomposed by performing NMF and the estimated speech and noise components are obtained. Finally, the obtained speech and noise components are used to enhance the noisy speech. Moreover, the residual noise is further eliminated by applying the speech presence probability (SPP). The objective evaluations demonstrate that the proposed algorithm outperforms the conventional NMF based method for all the evaluated noise types at various input signal-to-noise ratios." @default.
- W2890828644 created "2018-09-27" @default.
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- W2890828644 date "2018-07-01" @default.
- W2890828644 modified "2023-10-16" @default.
- W2890828644 title "Speech Enhancement Based on Codebook Constrained Nonnegative Matrix Factorization" @default.
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- W2890828644 doi "https://doi.org/10.1109/icalip.2018.8455570" @default.
- W2890828644 hasPublicationYear "2018" @default.
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