We introduce a new paradigm for active sound modification: Active Speech Enhancement (ASE). While Active Noise Cancellation (ANC) algorithms focus on suppressing external interference, ASE goes further by actively shaping the speech signal -- both attenuating unwanted noise components and amplifying speech-relevant frequencies -- to improve intelligibility and perceptual quality. To enable this, we propose a novel Transformer-Mamba-based architecture, along with a task-specific loss function designed to jointly optimize interference suppression and signal enrichment. Our method outperforms existing baselines across multiple speech processing tasks -- including denoising, dereverberation, and declipping -- demonstrating the effectiveness of active, targeted modulation in challenging acoustic environments.
@article{arxiv.2505.16911,
title = {Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation},
author = {Ofir Yaish and Yehuda Mishaly and Eliya Nachmani},
journal= {arXiv preprint arXiv:2505.16911},
year = {2025}
}