主动增强语音信号，同时降噪、去混响、修复失真
Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation
- 用Transformer-Mamba融合架构实现语音主动增强
- 在降噪、去混响、去削波任务中均超越现有方法
- 适合需要高质量语音处理的实时通信场景
我们提出一种新的主动声音调节范式——主动语音增强（ASE）。与仅抑制外部干扰的主动降噪（ANC）不同，ASE主动塑造语音信号，既抑制噪声成分，又增强语音相关频段，以提升可懂度和听感质量。为此，我们设计了一种新型的Transformer-Mamba混合架构，并引入专用于多任务优化的损失函数，联合优化干扰抑制与信号增强。实验表明，该方法在多个语音处理任务中表现优异，包括降噪、去混响和去削波，在复杂声学环境下展现了主动、定向调制的有效性。
原文摘要 · Abstract (English)
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.
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