Smartphone-Based Open Research Platform for Hearing Improvement Studies
DISCLAIMER STATEMENTS
This research is supported by National Institutes of Health (NIH)-National Institute on Deafness and Other communication Disorders (NIDCD) under award number R01DC015430.The content of this website is solely the responsibility of the contributors and does not necessarily represent the official views of the National Institutes of Health.
The information and materials contained in this website is a presentation of the documented research work carried out by the faculty, students and personnel at the University of Texas at Dallas (UTD). This website including its content is available for public access with the understanding that UTD and the authorized faculty and students contributing to this website make no warranties, either expressed or implied, concerning the completeness, reliability, or suitability of the presented materials for any kind of applications. Neither UTD nor any contributor to this website and its content shall be held liable to any party for any use or misuse of the information and materials contained in this website in any form or shape. Nor does the UTD warrant that the use of this website information is free of any claims of copyright infringement. This website does not endorse any commercial providers or their products. UTD and faculty managing this website reserve the right to remove, update, alter, or take down any and all posted materials on this website at any time without notice.
This page provides the codes (MATLAB, C/C++, Java, Objective-C files) used in this project.
Please click on the zip and press download button in the opening page to download the compressed files. Please refer to the User Guides for guidelines.
– DOA: Direction of Arrival Estimation and Speech Source Localization
– SE: Speech Enhancement (including the speech classification and clinical testing)
– ASDS: Alert Signal Detection and Separation
– AFC: Acoustic Feedback Cancellation
– COMP: Compression, Fitting
– VAD: Voice Activation Detection
– FMIC: Framework for using Smartphone Microphones
– NC: Noise Classification
– GUI: Graphical User Interface
– SS: Speech Separation
– WASN: Wireless Acoustic Sensor Network
Please contact Issa Panahi (issa.panahi@utdallas.edu) for the codes.
- SS: DSENet: Directional Signal Extraction Network for Hearing Improvement on Edge Devices
- SS: UX-NET: Filter-and-Process-based Improved U-Net for Real-time Time-domain Audio Separation
Please contact Issa Panahi (issa.panahi@utdallas.edu) for the codes.
- DOA: Robust Three-Microphone Speech Source Localization Using Randomized Singular Value Decomposition
- WASN: Joint Calibration and Synchronization of Two Arrays of Microphones and Loudspeakers Using Particle Swarm Optimization
- COMP: Development and Pilot Testing of Smartphone-Based Hearing Test Application
- SE: Real-time joint dereverberation and speech enhancement for hearing aid applications using edge devices
Please contact Issa Panahi (issa.panahi@utdallas.edu) for the codes.
- ASDS: Alert signal detection and integration to speech enhancement (MATLAB feature extraction, Python training and iOS implementation codes)
- SE: Minimum Variance Distortionless Response (MVDR) + Speech Enhancement (MATLAB, Android implementation).
- SE: Speech Enhancement (SE) super-Gaussian joint maximum a posteriori (SGJMAP) – SHARP 1 (MATLAB, iOS implementation).
- SE:Convolutional Neural network (CNN) based speech enhancement (MATLAB feature extraction, Python training and iOS implementation codes).
- SE: Efficient two-microphone speech enhancement using basic recurrent neural network cell for hearing and hearing aids
- SE: Real-time single-channel deep neural network-based speech enhancement on edge devices
- VAD: AutoML based voice activity detector (MATLAB feature extraction, iOS implementation codes).
- DOA: Feature extraction, Training and Android Implementation for Deep Neural Network (DNN) based Two Microphone DOA (MATLAB, Python TensorFlow, Android implementation)
- DOA: Feature extraction, Training and Android Implementation for Convolutional Neural Network (CNN) based Two Microphone DOA (MATLAB, Python TensorFlow, Android implementation)
- DOA: Convolutional Recurrent Neural Network Based Direction of Arrival Estimation Method Using Two Microphones for Hearing Studies
- DOA: Real-Time Estimation of Direction of Arrival of Speech Source using Three Microphones
- DOA: Spectral Flux-Based Convolutional Neural Network Architecture for Speech Source Localization and its Real-Time Implementation
- COMP: FFT based Multi-band Adaptive Dynamic Range Compression (MATLAB and iOS Implementation)
- AFC: Adaptive Feedback Cancellation using Adaptive Noise Injection – Noise Injection based approach (MATLAB, Android & iOS Implementation)
2018
2017
