Active Audio Cancellation with Multi-Band Mamba Network

Yehuda Mishaly, Lior Wolf, Eliya Nachmani

Keywords: Active Noise Cancellation, Audio, Speech, Mamba

TL;DR: Active Audio Cancellation with Mamba architecture

Abstract:

A novel deep learning approach for Active Audio Cancellation (AAC) is presented, which surpasses traditional Active Noise Cancellation (ANC) by effectively canceling any audio signal, regardless of its spectral content. We propose, for the first time, a deep learning approach to AAC using a novel multi-band Mamba architecture. This architecture partitions input audio into multiple frequency bands, allowing for precise anti-signal generation and enhanced phase alignment across frequencies, thereby improving overall cancellation performance. Additionally, we introduce an optimization-driven loss function that provides near-optimal supervisory signals for anti-signal generation. Our experimental results demonstrate substantial improvements over existing methods, achieving up to 7.2dB gain in ANC scenarios and up to 6.2dB improvement in AAC for voice audio signals, outperforming existing methods.

Primary Area: applications to computer vision, audio, language, and other modalities

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 906