  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.
