ISCA Archive Interspeech 2023
ISCA Archive Interspeech 2023

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

Chang Han, Xinmeng Xu, Weiping Tu, Yuhong Yang, Yajie Liu

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.