Smartphone based real time Super Gaussian Speech enhancement to improve intelligibility for hearing aid users using formant information.
This GitHub repository provides for Speech enhancement on iOS smartphone platforms. The example app provided here is for hearing improvement studies.
Abstract: In this paper, we present a Speech Enhancement (SE) technique to improve intelligibility of speech perceived by Hearing Aid users using smartphone as an assistive device. We use the formant frequency information to improve the overall quality and intelligibility of the speech. The proposed SE method is based on new super Gaussian joint maximum a Posteriori (SGJMAP) estimator. Using the priori information of formant frequency locations, the derived gain function has “tradeoff” factors that allows the smartphone user to customize perceptual preference, by controlling the amount of noise suppression and speech distortion in real-time. The formant frequency information helps the hearing aid user to control the gains over the non-formant frequency band, allowing the HA users to attain more noise suppression while maintaining the speech intelligibility using a smartphone application. Objective intelligibility measures and subjective results reflect the usability of the developed SE application in noisy real world acoustic environment.
You can find the paper for this GitHub repository : https://ieeexplore.ieee.org/document/8513674
[iOS] (User’s Guide- iOS (FSGJMAP).pdf)
- iPhone 7 running iOS 10.3
The codes are licensed under open-source MIT license.
For any utilization of the code content of this repository, one of the following books needs to get cited by the user:
- G. S. Bhat, C. K. A. Reddy, N. Shankar and I. M. S. Panahi, "Smartphone based real-time super Gaussian single microphone Speech Enhancement to improve intelligibility for hearing aid users using formant information," 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, 2018, pp. 5503-5506, doi: 10.1109/EMBC.2018.8513674.
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