Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation
This addresses speech intelligibility and quality issues in noisy environments, representing a novel paradigm rather than an incremental improvement.
The paper tackles the problem of enhancing speech by actively shaping the signal to suppress noise and amplify speech frequencies, achieving improved performance in denoising, dereverberation, and declipping tasks compared to existing baselines.
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.