Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation

TL;DR

This paper introduces Active Speech Enhancement (ASE), a novel approach that actively modifies speech signals by suppressing noise and enhancing speech components using a Transformer-based architecture, improving intelligibility in noisy environments.

Contribution

The paper presents a new paradigm for active sound modification and a Transformer-Mamba-based model with a task-specific loss for joint noise suppression and speech enhancement.

Findings

Outperforms existing baselines in denoising, dereverberation, and declipping

Demonstrates effectiveness of active modulation in challenging environments

Improves speech intelligibility and perceptual quality

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, 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.