The task of single-channel speech enhancement is to restore clean speech from noisy speech. Recently, speech enhancement has been greatly improved with the introduction of deep learning. Previous work proved that using ideal ratio mask or phase-sensitive mask as intermediation to recover clean speech can yield better performance. In this case, the mean square error is usually selected as the loss function. However, after conducting experiments, the authors find that the mean square error has a problem. It considers absolute error values, meaning that the gradients of the network depend on absolute differences between estimated values and true values, so the points in magnitude spectra with smaller values contribute little to the gradients. To solve this problem, they propose relative loss, which pays more attention to relative differences between magnitude spectra, rather than the absolute differences, and is more in accordance with human sensory characteristics. The perceptual evaluation of speech quality, the short-time objective intelligibility, the signal-to-distortion ratio, and the segmental signal-to-noise ratio are used to evaluate the performance of the relative loss. Experimental results show that it can greatly improve speech enhancement by focusing on smaller values.

中文翻译：

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通过使用相对损耗关注较小的值来改善语音增强

单通道语音增强的任务是从嘈杂的语音中恢复干净的语音。最近，随着深度学习的引入，语音增强已经得到了极大的改善。先前的工作证明，使用理想比率蒙版或相敏蒙版作为中介来恢复干净的语音可以产生更好的性能。在这种情况下，通常选择均方误差作为损失函数。但是，在进行实验后，作者发现均方误差存在问题。它考虑了绝对误差值，这意味着网络的梯度取决于估计值和真实值之间的绝对差，因此幅值谱中具有较小值的点对梯度的贡献很小。为了解决这个问题，他们提出了相对损失，它更多地关注幅度谱之间的相对差异，而不是绝对差异，并且更符合人类的感官特征。语音质量的感知评估，短期目标清晰度，信噪比和分段信噪比用于评估相对损耗的性能。实验结果表明，通过关注较小的值，它可以极大地改善语音增强。