论文标题
多目标深度学习语音剥夺方法的评论
A Review of Multi-Objective Deep Learning Speech Denoising Methods
论文作者
论文摘要
本文介绍了文献中介绍的多项式深度学习方法的评论。在陈述了传统，单一客观的深度学习以及混合或组合的常规和深度学习方法之后，提供了对多目标深度学习方法的数学框架的回顾。选择了每个语音denoising类别的代表性方法，其代码已公开可用，并通过考虑相同的公共领域数据集和四个广泛使用的目标指标来进行比较。比较结果表明，与其他方法相比，多目标方法的有效性，特别是当信噪比较低时。还提到了可以实现的未来改进。
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.
