Abstract
This paper presents a self-supervised deep neural network solution to speech denoising by easing the requirement that clean speech signals need to be available for network training. This self-supervised approach is based on training a Fully Convolutional Neutral Network to map a noisy speech signal to another noisy version of the speech signal. To show the effectiveness of the developed approach, four commonly used objective performance measures are used to compare the self-supervised approach to the commonly used fully-supervised approach in which it is assumed that clean speech signals are available for training. The measures are examined for three public domain datasets of speech signals and one public domain dataset of noise signals. The results obtained indicate the self-supervised approach outperforms the fully-supervised approach. This solution is more suited for field deployment compared to the conventional deep learning-based solutions since under realistic audio conditions the only signals which are available for training are noisy speech signals and not clean speech signals.
Abstract (translated)
本文提出了一种基于自监督的深度神经网络的语音去噪方法，该方法放宽了网络训练需要干净语音信号的要求。这种自监督方法是基于训练一个完全卷积的中性网络，将一个有噪声的语音信号映射到另一个有噪声的语音信号版本。为了证明所开发方法的有效性，采用了四种常用的客观绩效指标，将自我监督方法与常用的完全监督方法进行了比较，其中假设干净的语音信号可用于培训。对三个语音信号公共域数据集和一个噪声信号公共域数据集进行了测试。结果表明，自监督方法优于完全监督方法。与传统的基于深度学习的解决方案相比，此解决方案更适合现场部署，因为在实际音频条件下，可用于培训的唯一信号是噪音语音信号，而不是干净的语音信号。
URL
https://arxiv.org/abs/1904.12069
