Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression

We choose the dual-path transformer-based full-subband network (DPT-FSNet) [12] to explore compression methods for Echo cancellation and noise reduction are essential for fullduplex three reasons. First, the model has exhibited high wide-band communication, yet most existing neural networks have perceptual evaluation of speech quality (WB-PESQ) scores on high computational costs and are inflexible in tuning model the NS task with a small number of parameters but suffers from complexity. In this paper, we introduce time-frequency dualpath large computational cost. Second, DPT-FSNet is conducted compression to achieve a wide range of compression ratios on complete time-frequency (T-F) feature maps, indicating its on computational cost. Specifically, for frequency compression, complexity being closely related to the number of frames and trainable filters are used to replace manually designed frequency bands. Third, the model involves a 2D convolution filters for dimension reduction. For time compression, only using encoder, a dual-path transformer and a 2D convolution decoder, frame skipped prediction causes large performance degradation, implying that compression methods should be applicable to different which can be alleviated by a post-processing network modules.