Deep Active Speech Cancellation with Multi-Band Mamba Network
Journal:
arXiv
Published Date:
Feb 3, 2025
Abstract
We present a novel deep learning network for Active Speech Cancellation
(ASC), advancing beyond Active Noise Cancellation (ANC) methods by effectively
canceling both noise and speech signals. The proposed Multi-Band Mamba
architecture segments input audio into distinct frequency bands, enabling
precise anti-signal generation and improved phase alignment across frequencies.
Additionally, we introduce an optimization-driven loss function that provides
near-optimal supervisory signals for anti-signal generation. Experimental
results demonstrate substantial performance gains, achieving up to 7.2dB
improvement in ANC scenarios and 6.2dB in ASC, significantly outperforming
existing methods. Audio samples are available at
https://mishalydev.github.io/DeepASC-Demo
