Echo cancellation and noise reduction are essential for full-duplex communication, yet most existing neural networks have high computational costs and are inflexible in tuning model complexity. In this paper, we introduce time-frequency dual-path compression to achieve a wide range of compression ratios on computational cost. Specifically, for frequency compression, trainable filters are used to replace manually designed filters for dimension reduction. For time compression, only using frame skipped prediction causes large performance degradation, which can be alleviated by a post-processing network with full sequence modeling. We have found that under fixed compression ratios, dual-path compression combining both the time and frequency methods will give further performance improvement, covering compression ratios from 4x to 32x with little model size change. Moreover, the proposed models show competitive performance compared with fast FullSubNet and DeepFilterNet.

翻译：回声消除与噪声抑制是实现全双工通信的关键技术，然而现有大多数神经网络存在计算成本高且模型复杂度调节不灵活的问题。本文引入时频双路径压缩技术，能够在计算成本上实现大范围的压缩比。具体而言，在频率压缩方面，采用可训练滤波器替代人工设计滤波器以实现降维；在时间压缩方面，仅使用跳帧预测会导致显著的性能下降，而通过采用全序列建模的后处理网络可缓解该问题。研究发现，在固定压缩比下，融合时间与频率方法的双路径压缩能进一步提升性能，在模型尺寸几乎不变的情况下覆盖4倍至32倍的压缩比。此外，所提模型在性能上与快速FullSubNet和DeepFilterNet相比展现出竞争力。