Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation

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.