Smartphone-based single-channel speech enhancement application for hearing aids.

This work presents a single-channel speech enhancement (SE) framework based on the super-Gaussian extension of the joint maximum a posteriori (SGJMAP) estimation rule. The developed SE algorithm is an open-source research smartphone-based application for hearing improvement studies. In this algorithm, the SGJMAP-based estimation for noisy speech mixture is smoothed along the frequency axis by a Mel filter-bank, resulting in a Mel-warped frequency-domain SGJMAP estimation.

Real-time single-channel deep neural network-based speech enhancement on edge devices.

In this paper, we present a deep neural network architecture comprising of both convolutional neural network (CNN) and recurrent neural network (RNN) layers for real-time single-channel speech enhancement (SE). The proposed neural network model focuses on enhancing the noisy speech magnitude spectrum on a frame-by-frame process. The developed model is implemented on the smartphone (edge device), to demonstrate the real-time usability of the proposed method.

Behavioral Validation of the Smartphone for Remote Microphone Technology.

As part of a National Institutes of Health-National Institute on Deafness and Other communication Disorders (NIH-NIDCD)-supported project to develop open-source research and smartphone-based apps for enhancing speech recognition in noise, an app called Smartphone Hearing Aid Research Project Version 2 (SHARP-2) was tested with persons with normal and impaired hearing when using three sets of hearing aids (HAs) with wireless connectivity to an iPhone. Participants were asked to type sentences presented from a speaker in front of them while hearing noise from behind in two conditions, HA alone and HA + SHARP-2 app running on the iPhone. The signal was presented at a constant level of 65 dBA and the signal-to-noise ratio varied from -10 to +10, so that the task was difficult when listening through the bilateral HAs alone.

Automated machine learning based speech classification for hearing aid applications and its real-time implementation on smartphone.

Deep neural networks (DNNs) have been useful in solving benchmark problems in various domains including audio. DNNs have been used to improve several speech processing algorithms that improve speech perception for hearing impaired listeners. To make use of DNNs to their full potential and to configure models easily, automated machine learning (AutoML) systems are developed, focusing on model optimization.

Real-time dual-channel speech enhancement by VAD assisted MVDR beamformer for hearing aid applications using smartphone.

In this paper, a dual-channel speech enhancement (SE) method is proposed. The proposed method is a combination of minimum variance distortionless response (MVDR) beamformer and a super-Gaussian joint maximum a posteriori (SGJMAP) based SE gain function. The proposed SE method runs on a smartphone in real-time, providing a portable device for hearing aid (HA) applications.

A Computationally efficient IVA-based Blind Source Separation for Hearing Aid Applications and its Real-time Implementation on Smartphone.

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.

Design and Integration of Alert Signal Detector and Separator for Hearing Aid Applications.

Alert signals like sirens and home alarms are important as they warn people of precarious situations. This work presents the detection and separation of these acoustically important alert signals, not to be attenuated as noise, to assist the hearing impaired listeners. The proposed method is based on convolutional neural network (CNN) and convolutional-recurrent neural network (CRNN).

Efficient two-microphone speech enhancement using basic recurrent neural network cell for hearing and hearing aids.

This work presents a two-microphone speech enhancement (SE) framework based on basic recurrent neural network (RNN) cell. The proposed method operates in real-time, improving the speech quality and intelligibility in noisy environments. The RNN model trained using a simple feature set-real and imaginary parts of the short-time Fourier transform (STFT) are computationally efficient with a minimal input-output processing delay.

A Real-Time Convolutional Neural Network Based Speech Enhancement for Hearing Impaired Listeners Using Smartphone.

This paper presents a Speech Enhancement (SE) technique based on multi-objective learning convolutional neural network to improve the overall quality of speech perceived by Hearing Aid (HA) users. The proposed method is implemented on a smartphone as an application that performs real-time SE. This arrangement works as an assistive tool to HA.

Smartphone based real-time super Gaussian single microphone Speech Enhancement to improve intelligibility for hearing aid users using formant information.

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.

Influence of MVDR beamformer on a Speech Enhancement based Smartphone application for Hearing Aids.

This paper presents the minimum variance distortionless response (MVDR) beamformer combined with a Speech Enhancement (SE) gain function as a real-time application running on smartphones that work as an assistive device to Hearing Aids. It has been shown that beamforming techniques improve the Signal to Noise Ratio (SNR) in noisy conditions. In the proposed algorithm, MVDR beamformer is used as an SNR booster for the SE method.

An individualized super-Gaussian single microphone Speech Enhancement for hearing aid users with smartphone as an assistive device.

In this letter, we derive a new super Gaussian Joint Maximum (SGJMAP) based single microphone speech enhancement gain function. The developed Speech Enhancement method is implemented on a smartphone, and this arrangement functions as an assistive device to hearing aids. We introduce a "" parameter in the derived gain function that allows the smartphone user to customize their listening preference, by controlling the amount of noise suppression and speech distortion in real-time based on their level of hearing comfort perceived in noisy real world acoustic environment.

Formant Frequency-based Speech Enhancement Technique to improve Intelligibility for hearing aid users with smartphone as an assistive device.

In this paper, we present a Speech Enhancement (SE) method implemented on a smartphone, and this arrangement functions as an assistive device to hearing aids (HA). Many benchmark single channel SE algorithms implemented on HAs provide considerable improvement in speech quality, while speech intelligibility improvement still remains a prime challenge. The proposed SE method based on Log spectral amplitude estimator improves speech intelligibility in the noisy real world acoustic environment using the priori information of formant frequency locations.