Modality-Specific Speech Enhancement and Noise-Adaptive Fusion for Acoustic and Body-Conduction Microphone Framework

• URL: http://arxiv.org/abs/2508.17336v2
• Date: Thu, 28 Aug 2025 02:26:32 GMT
• Title: Modality-Specific Speech Enhancement and Noise-Adaptive Fusion for Acoustic and Body-Conduction Microphone Framework
• Authors: Yunsik Kim, Yoonyoung Chung,
• Abstract summary: Body-conduction microphone signals (BMS) bypass airborne sound, providing strong noise resistance.<n>We propose a novel multi-modal framework that combines BMS and acoustic microphone signals (AMS) to achieve both noise suppression and high-frequency reconstruction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Body-conduction microphone signals (BMS) bypass airborne sound, providing strong noise resistance. However, a complementary modality is required to compensate for the inherent loss of high-frequency information. In this study, we propose a novel multi-modal framework that combines BMS and acoustic microphone signals (AMS) to achieve both noise suppression and high-frequency reconstruction. Unlike conventional multi-modal approaches that simply merge features, our method employs two specialized networks: a mapping-based model to enhance BMS and a masking-based model to denoise AMS. These networks are integrated through a dynamic fusion mechanism that adapts to local noise conditions, ensuring the optimal use of each modality's strengths. We performed evaluations on the TAPS dataset, augmented with DNS-2023 noise clips, using objective speech quality metrics. The results clearly demonstrate that our approach outperforms single-modal solutions in a wide range of noisy environments.

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