Frequency modulation (FM) is a form of radio broadcasting which is widely used nowadays and has been for almost a century. We suggest a software-defined-radio (SDR) receiver for FM demodulation that adopts an end-to-end learning based approach and utilizes the prior information of transmitted speech message in the demodulation process. The receiver detects and enhances speech from the in-phase and quadrature components of its base band version. The new system yields high performance detection for both acoustical disturbances, and communication channel noise and is foreseen to out-perform the established methods for low signal to noise ratio (SNR) conditions in both mean square error and in perceptual evaluation of speech quality score.
@article{arxiv.1704.02046,
title = {End to End Deep Neural Network Frequency Demodulation of Speech Signals},
author = {Dan Elbaz and Michael Zibulevsky},
journal= {arXiv preprint arXiv:1704.02046},
year = {2017}
}