Active speech enhancement: beyond passive denoising declipping and dereverberation

Ofir Yaish, Yehuda Mishaly, Eliya Nachmani

Keywords: Active Noise Cancellation, ANC, Transformer, Mamba, Dereverberation, Declipping, NLP, Speech

TL;DR: Active speech enhancement and noise suppression

Abstract: We introduce a new paradigm for active sound modification: Active Speech Enhancement (ASE). While Active Noise Cancellation (ANC) algorithms focus on suppressing external interference and traditional speech enhancement passively reconstructs degraded speech signals, 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 in an acoustic environment. 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. A demo page and source code are provided in the Supplementary Materials.

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

Submission Number: 2082