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End to End Deep Neural Network Frequency Demodulation of Speech Signals

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Advances in Information and Communication Networks (FICC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 886))

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Abstract

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.

This research was supported by the Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI).

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Authors and Affiliations

  1. Department of Computer Science, Technion Israel Institute of Technology, 32000, Haifa, Israel

    Dan Elbaz & Michael Zibulevsky

Authors
  1. Dan Elbaz
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  2. Michael Zibulevsky
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Corresponding author

Correspondence to Dan Elbaz .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, London, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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Elbaz, D., Zibulevsky, M. (2019). End to End Deep Neural Network Frequency Demodulation of Speech Signals. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 886. Springer, Cham. https://doi.org/10.1007/978-3-030-03402-3_1

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