Redefining Single-Channel Speech Enhancement: The xLSTM-SENet Approach
Speech processing systems often struggle to deliver clear audio in noisy environments. This challenge impacts applications such as hearing aids, automatic speech recognition (ASR), and speaker verification. Conventional single-channel speech enhancement (SE) systems use neural network architectures like LSTMs, CNNs, and GANs, but they are not without limitations. For instance, attention-based models such as Conformers, […]
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Summary
The article discusses the challenges faced by speech processing systems in delivering clear audio in noisy environments, impacting applications like hearing aids and automatic speech recognition. It introduces the xLSTM-SENet approach as a solution to single-channel speech enhancement, highlighting the limitations of conventional neural network architectures like LSTMs, CNNs, and GANs. The xLSTM-SENet approach aims to redefine speech enhancement by addressing these limitations.
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