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arXiv · 2307.13888

Exploring the Interactions between Target Positive and Negative Information for Acoustic Echo Cancellation

Chang Han·Xinmeng Xu·Weiping Tu·Yuhong Yang·Yajie Liu
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Abstract

Acoustic echo cancellation (AEC) aims to remove interference signals while leaving near-end speech least distorted. As the indistinguishable patterns between near-end speech and interference signals, near-end speech can't be separated completely, causing speech distortion and interference signals residual. We observe that besides target positive information, e.g., ground-truth speech and features, the target negative information, such as interference signals and features, helps make pattern of target speech and interference signals more discriminative. Therefore, we present a novel AEC model encoder-decoder architecture with the guidance of negative information termed as CMNet. A collaboration module (CM) is designed to establish the correlation between the target positive and negative information in a learnable manner via three blocks: target positive, target negative, and interactive block. Experimental results demonstrate our CMNet achieves superior performance than recent methods.

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Chang Han, Xinmeng Xu, Weiping Tu, Yuhong Yang, Yajie Liu. 2023-07-26. Exploring the Interactions between Target Positive and Negative Information for Acoustic Echo Cancellation. https://arxiv.org/abs/2307.13888

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