  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.

Audio and Speech Processing;Machine Learning;Sound