This paper presents a review of multi-objective deep learning methods that have been introduced in the literature for speech denoising. After stating an overview of conventional, single objective deep learning, and hybrid or combined conventional and deep learning methods, a review of the mathematical framework of the multi-objective deep learning methods for speech denoising is provided. A representative method from each speech denoising category, whose codes are publicly available, is selected and a comparison is carried out by considering the same public domain dataset and four widely used objective metrics. The comparison results indicate the effectiveness of the multi-objective method compared with the other methods, in particular when the signal-to-noise ratio is low. Possible future improvements that can be achieved are also mentioned.

中文翻译：

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多目标深度学习语音降噪方法综述

本文回顾了文献中引入的用于语音去噪的多目标深度学习方法。在概述了传统、单目标深度学习以及混合或组合传统和深度学习方法之后，提供了用于语音去噪的多目标深度学习方法的数学框架的回顾。从每个语音去噪类别中选择一种其代码公开可用的代表性方法，并通过考虑相同的公共领域数据集和四个广泛使用的客观指标进行比较。比较结果表明多目标方法与其他方法相比的有效性，特别是在信噪比低时。还提到了未来可能实现的改进。