2021
DOI: 10.1109/lsp.2021.3070734
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Split Bregman Approach to Linear Prediction Based Dereverberation With Enforced Speech Sparsity

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Cited by 10 publications
(2 citation statements)
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“…One such method is the weighted prediction error (WPE) method [11], which has demonstrated its effectiveness in dereverberation. To further enhance performance, works such as [16] and [17] propose leveraging speech sparsity in the time-frequency domain to incorporate an additional prior on the unknown variance. Despite the clear physical interpretation of these model-based methods, their performance and robustness are limited as they do not fully exploit the inherent priors of speech signal structures.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…One such method is the weighted prediction error (WPE) method [11], which has demonstrated its effectiveness in dereverberation. To further enhance performance, works such as [16] and [17] propose leveraging speech sparsity in the time-frequency domain to incorporate an additional prior on the unknown variance. Despite the clear physical interpretation of these model-based methods, their performance and robustness are limited as they do not fully exploit the inherent priors of speech signal structures.…”
Section: Introductionmentioning
confidence: 99%
“…Specifically, we formulate the prediction error minimization problem of WPE with an additional regularizer that is not explicitly handcrafted. Unlike vanilla WPE and its extensions [16], [17], [34], [35], which do not consider sophisticated speech priors, we propose integrating speech prior information by employing the plugand-play strategy. Specifically, we employ the regularization by denoising (RED) strategy, which is a promising variant of PnP [36].…”
Section: Introductionmentioning
confidence: 99%