MIMO Speech Compression and Enhancement Based on Convoltuional Denoising Autoencoder
You-Jin Li, Syu‐Siang Wang, Yu Tsao, Borching Su · arXiv (Cornell University) · 2020
For speech-related applications in Internet of things environments, identifying effective methods to handle interference noises and compress the amount of data in transmissions is essential for achieving high-quality services. In this paper, we propose a novel multi-input multi-output speech compression and enhancement (MIMO-SCE) system based on a convolutional denoising auto-encoder (CDAE) model to simultaneously improve speech quality and reduce the dimension of transmission data. Compared with conventional single-channel and multi-input single-output systems, MIMO systems can be employed for applications where multiple acoustic signals need to be handled. We investigated two CDAE models, fully convolutional network (FCN) and 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 to one-seventh for transmission.