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. A demo page can be found at hangtingchen.github.io/ultra_dual_path_compression.github.io/.

翻译：回声消除与噪声抑制是全双工通信中的关键技术，但现有神经网络普遍存在计算成本高、模型复杂度调节不灵活的问题。本文提出时频双路径压缩方法，可在计算成本维度实现宽范围压缩比。具体而言，针对频率压缩，采用可训练滤波器替代人工设计滤波器进行降维；针对时间压缩，仅使用帧跳跃预测会导致显著性能退化，而通过含完整序列建模的后处理网络可缓解该问题。实验表明，在固定压缩比下，结合时间与频率方法的双路径压缩能进一步提升性能，可在模型尺寸几乎不变的情况下实现4倍至32倍的压缩比覆盖。此外，所提模型在与快速FullSubNet及DeepFilterNet的对比中展现出竞争力。演示页面详见hangtingchen.github.io/ultra_dual_path_compression.github.io/。