基于卷积去噪自编码器的MIMO语音压缩与增强
MIMO Speech Compression and Enhancement Based on Convolutional Denoising Autoencoder
摘要：
对于物联网环境中与语音相关的应用，确定有效的方法来处理干扰噪声和压缩传输中的数据量对于实现高质量的服务至关重要。在这项研究中，我们提出了一种新的多输入多输出语音压缩和增强（MIMO-SCE）系统的卷积去噪自动编码器（CDAE）模型的基础上，同时提高语音质量和减少传输数据的维数。与传统的单通道和多输入单输出系统相比，MIMO系统可以用于处理需要处理的多个声学信号的应用中。我们研究了两种CDAE模型，全卷积网络（FCN）和Sinc FCN，作为MIMO系统中的核心模型。实验结果证实，所提出的MIMO-SCE框架有效地提高了语音质量和可懂度，同时减少了7倍的记录数据量进行传输。
英文摘要：
For speech-related applications in IoT environments, identifying effective methods to handle interference noises and compress the amount of data in transmissions is essential to achieve high-quality services. In this study, we propose a novel multi-input multi-output speech compression and enhancement (MIMO-SCE) system based on a convolutional denoising autoencoder (CDAE) model to simultaneously improve speech quality and reduce the dimensions of transmission data. Compared with conventional single-channel and multi-input single-output systems, MIMO systems can be employed in applications that handle multiple acoustic signals need to be handled. We investigated two CDAE models, a fully convolutional network (FCN) and a Sinc FCN, as the core models in MIMO systems. The experimental results confirm that the proposed MIMO-SCE framework effectively improves speech quality and intelligibility while reducing the amount of recording data by a factor of 7 for transmission.
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