Single-channel speech enhancement using learnable loss mixup
Oscar Chang Dung N. Tran Kazuhito Koishida
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.