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- W4313443836 abstract "In this paper, a novel single-channel speech enhancement algorithm based on an iterative posterior regularized nonnegative matrix factorization (NMF)-based adaptive Wiener filter is proposed. The statistical-based approaches’ performance is poor under the non-stationary background noise conditions. Prior information is needed for template-based methods, but performance-wise template-based methods are robust and better in presence of non-stationary compared to statistical model-based approaches. To overcome the limitations of both, we propose a method that combines the iterative posterior regularized NMF and an adaptive Wiener filtering method. In the proposed method, prior distributions are used for transformed domain spectral magnitudes of speech and noise components to model an iterative posterior NMF. The magnitude of the spectral component of speech and noise is considered as an inverse gamma, Gaussian distribution. The samples of speech and noise are well fit in those distributions to provide a better adaptive estimation of the necessary statistics of those distributions to get a natural regularization of the NMF criterion. An adaptive Wiener filter technique derives the adaptive factor (α) based on the signal-to-noise ratio (SNR) level for the Wiener filter gain. The adaptive factor minimizes the estimation errors of the Wiener filter and improves the speech quality by adjusting the base vector weights based on estimated noise and speech levels. The proposed method outperforms other benchmark algorithms in terms of the source to distortion ratio (SDR), short-time objective intelligibility (STOI), and perceptual evaluation of speech quality (PESQ)." @default.
- W4313443836 created "2023-01-06" @default.
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- W4313443836 date "2023-01-01" @default.
- W4313443836 modified "2023-09-30" @default.
- W4313443836 title "An Iterative Posterior Regularized NMF-Based Adaptive Wiener Filter for Speech Enhancement" @default.
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- W4313443836 doi "https://doi.org/10.1007/978-981-19-5868-7_42" @default.
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