一种用于基于深度神经网络的双耳语音增强的新型双耳线索保留损失函数
A Novel Binaural Cue Preservation Loss for DNN-Based Binaural Speech Enhancement
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中文总结 AI 辅助
针对基于DNN的双耳语音增强易失真左右信号关系的问题，提出两种新型双耳线索保留损失函数，实验证明其在保持降噪性能的同时减少了掩蔽失真，第二种联合损失在ILD保留上更优。
中文摘要 AI 辅助
助听器的双耳语音增强旨在减少噪声同时保留空间定位所需的双耳线索。尽管基于深度神经网络的方法实现了出色的降噪效果，但它们常使左右信号间的关系发生失真。本文提出两种新型双耳线索保留损失函数：第一种为双耳重构误差损失，直接惩罚掩蔽导致的左右频谱关系失真，相比以往研究中单独的耳间级差（ILD）和耳间相位差（IPD）误差，能更直接地衡量双耳一致性；第二种为双耳线索损失，联合建模ILD与IPD以更好保留双耳结构。实验结果表明，两种提出的损失函数均保持了出色的降噪性能，且相比最先进的基线线索损失减少了掩蔽导致的失真，同时第二种联合双耳线索损失在ILD保留上也优于基线。
英文摘要
Binaural speech enhancement for hearing aids aims to reduce noise while preserving the interaural cues needed for spatial localization. Although deep neural network-based methods achieve strong noise reduction, they often distort the rela- tionship between the left and right signals. In this paper, we propose two novel binaural cue preservation losses. First, a binaural reconstruction error loss that directly penalizes masking-induced distortion in the relationship between the left and right spectra, providing a more direct measure of the binaural consistency than conventional separate interaural level differences (ILD) and interaural phase differences (IPD) errors as in prior work. Second, a binaural cue loss that jointly models ILD and IPD to better preserve the binaural structure. Experimental results show that both proposed losses maintain strong noise reduction performance and reduce masking- induced distortion compared to the state-of-the-art baseline cue loss, while the second proposed joint binaural cue loss also outperforms the baseline in ILD preservation.
发表机构
- WSA
机构由 AI 辅助整理，请以论文原文为准。
