Single-channel speech enhancement using learnable loss mixup

• URL: http://arxiv.org/abs/2312.17255v1
• Date: Wed, 20 Dec 2023 00:25:55 GMT
• Title: Single-channel speech enhancement using learnable loss mixup
• Authors: Oscar Chang, Dung N. Tran, Kazuhito Koishida
• Abstract summary: Generalization remains a major problem in supervised learning of single-channel speech enhancement. We propose learnable loss mixup (LLM), a simple and effortless training diagram, to improve the generalization of deep learning-based speech enhancement models. Our experimental results on the VCTK benchmark show that learnable loss mixup 3.26 PESQ, achieves outperforming the state-of-the-art.
• Score: 23.434378634735676
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generalization remains a major problem in supervised learning of single-channel speech enhancement. In this work, we propose learnable loss mixup (LLM), a simple and effortless training diagram, to improve the generalization of deep learning-based speech enhancement models. Loss mixup, of which learnable loss mixup is a special variant, optimizes a mixture of the loss functions of random sample pairs to train a model on virtual training data constructed from these pairs of samples. In learnable loss mixup, by conditioning on the mixed data, the loss functions are mixed using a non-linear mixing function automatically learned via neural parameterization. Our experimental results on the VCTK benchmark show that learnable loss mixup achieves 3.26 PESQ, outperforming the state-of-the-art.

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