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arXiv · 2511.05945

Loud-loss: A Perceptually Motivated Loss Function for Speech Enhancement Based on Equal-Loudness Contours

Zixuan Li·Xueliang Zhang·Changjiang Zhao·Shuai Gao·Lei Miao·Zhipeng Yan·Ying Sun·Chong Zhu
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Abstract

The mean squared error (MSE) is a ubiquitous loss function for speech enhancement, but its problem is that the error cannot reflect the auditory perception quality. This is because MSE causes models to over-emphasize low-frequency components which has high energy, leading to the inadequate modeling of perceptually important high-frequency information. To overcome this limitation, we propose a perceptually-weighted loss function grounded in psychoacoustic principles. Specifically, it leverages equal-loudness contours to assign frequency-dependent weights to the reconstruction error, thereby penalizing deviations in a way aligning with human auditory sensitivity. The proposed loss is model-agnostic and flexible, demonstrating strong generality. Experiments on the VoiceBank+DEMAND dataset show that replacing MSE with our loss in a GTCRN model elevates the WB-PESQ score from 2.17 to 2.93-a significant improvement in perceptual quality.

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Zixuan Li, Xueliang Zhang, Changjiang Zhao, Shuai Gao, Lei Miao, Zhipeng Yan, Ying Sun, Chong Zhu. 2025-11-08. Loud-loss: A Perceptually Motivated Loss Function for Speech Enhancement Based on Equal-Loudness Contours. https://arxiv.org/abs/2511.05945

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