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A smartphone applications with Convolutional Neural Network Voice Activity Detector, Adaptive Noise Reduction and Dynamic Audio Range Compression

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SIP-Lab/Integrated-Hearing-Aid-App

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Integrating Signal Processing Modules of Hearing Aids into a Real-Time Smartphone App

This GitHub repository is the code accompaniment of the following paper:

Integrating Signal Processing Modules of Hearing Aids into a Real-Time Smartphone App
Tahsin A. Chowdhury, Abhishek Sehgal and Nasser Kehtarnavaz - University of Texas at Dallas
https://ieeexplore.ieee.org/abstract/document/8512970/

Abstract: This paper presents the integration of three major modules of the signal processing pipeline that go into a typical digital hearing aid as a real-time smartphone app. These modules include voice activity detection, noise reduction, and compression. The steps taken to allow the real-time implementation of this integration or signal processing pipeline are discussed. These steps can be utilized to create similar signal processing pipelines or integrated apps to evaluate hearing improvement algorithms. The real-time characteristics of the developed integrated app are reported as well as an objective evaluation of its noise reduction.

Resources

Supporting materials related to this work are available via the following links:

Link Description
https://ieeexplore.ieee.org/abstract/document/8512970 IEEE Manuscript
http://www.utdallas.edu/~kehtar/IntegratedApp.mp4 A videoclip demonstrating the settings and real-time operation of the developed integrated app

Getting Started

A User's Guide is provided which describes the codes for training and for real-time operation on Android and iOS smartphone platforms.

License and Citation

The codes are licensed under MIT license.

For any utilization of the code content of this repository, the following paper needs to get cited by the user:

  • T. Chowdhury, A. Sehgal and N. Kehtarnavaz, "Integrating Signal Processing Modules of Hearing Aids into a Real-Time Smartphone App," Proceedings of IEEE International Conference Engineering in Medicine and Biology (EMBC), pp. 2837-2840, Honolulu, 2018.

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A smartphone applications with Convolutional Neural Network Voice Activity Detector, Adaptive Noise Reduction and Dynamic Audio Range Compression

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