Real-time-Blind-source-separation-using-IVA

Overview

This GitHub repository provides for Speech enhancement on iOS smartphone platforms. The example app provided here is for hearing improvement studies.

Abstract: Conventional Blind Source Separation (BSS) techniques are computationally complex. This is due to the calculation of the demixing matrix for the entire signal or due to the frequent update of the demixing matrix at every time frame index, making them impractical to use in many Real-Time applications. In this paper, a robust, neural network based two-microphone sound source localization method is used as a criterion to enhance the efficiency of the Independent Vector Analysis (IVA), a BSS method. IVA is used to separate speech and noise sources which are convolutedly mixed. The practical usability of the proposed method is proved by implementing it on a smartphone to separate speech and noise in real-world scenarios for Hearing-Aid applications. The experimental results with objective and subjective tests reveal the usefulness of the developed method for real-world applications.

You can find the paper for this GitHub repository : https://asa.scitation.org/doi/pdf/10.1121/2.0001295

Users Guides

Android

Requirements

Pixel 1

License and Citation

The codes are licensed under MIT license.

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

Bhat, Gautam Shredhar, Chanan Karadagur Ananda Reddy, Nikhil Shankar, and Issa Panahi. "A Computationally efficient IVA-based Blind Source Separation for Hearing Aid Applications and its Real-time Implementation on Smartphone." In Proceedings of meetings on acoustics. Acoustical Society of America, vol. 39, no. 1, p. 055008. 2019.

Disclaimer

This work was supported in part by the National Institute of the Deafness and Other Communication Disorders (NIDCD) of the National Institutes of Health (NIH) under Award 1R01DC015430-02. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH