Task splitting for DNN-based acoustic echo and noise removal

Sebastian Braun; Maria Luis Valero

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arXiv · 2205.06931

Task splitting for DNN-based acoustic echo and noise removal

Sebastian Braun·Maria Luis Valero

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Abstract

Neural networks have led to tremendous performance gains for single-task speech enhancement, such as noise suppression and acoustic echo cancellation (AEC). In this work, we evaluate whether it is more useful to use a single joint or separate modules to tackle these problems. We describe different possible implementations and give insights into their performance and efficiency. We show that using a separate echo cancellation module and a module for noise and residual echo removal results in less near-end speech distortion and better performance during double-talk at same complexity.

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Sebastian Braun, Maria Luis Valero. 2022-05-13. Task splitting for DNN-based acoustic echo and noise removal. https://arxiv.org/abs/2205.06931

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