DOI: 10.54337/aau519583502
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Data-driven Speech Enhancement: from Non-negative Matrix Factorization to Deep Representation Learning

Abstract: In natural listening environments, speech signals are easily distorted by various acoustic interference, which reduces the speech quality and intelligibility of human listening; meanwhile, it makes difficult for many speech-related applications, such as automatic speech recognition (ASR). Thus, many speech enhancement (SE) algorithms have been developed in the past decades. However, most current SE algorithms are difficult to capture underlying speech information (e.g., phoneme) in the SE process. This causes … Show more

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