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Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 952-955, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018142


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. Spectral Flux based voice activity detector (VAD) is used to improve the accuracy of the beamformer output. The efficiency of the proposed SE method is evaluated using speech quality and intelligibility measures and compared with that of other SE techniques. The objective and subjective test results show the capability of the proposed SE method in three different noisy conditions at low signal to noise ratios (SNRs) of -5, 0, and +5 dB.

Auxiliares de Audição , Smartphone , Voz , Humanos , Ruído , Inteligibilidade da Fala
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 956-959, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018143


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. As an application of AutoML to audio and hearing aids, this work presents an AutoML based voice activity detector (VAD) that is implemented on a smartphone as a real-time application. The developed VAD can be used to elevate the performance of speech processing applications like speech enhancement that are widely used in hearing aid devices. The classification model generated by AutoML is computationally fast and has minimal processing delay, which enables an efficient, real-time operation on a smartphone. The steps involved in real-time implementation are discussed in detail. The key contribution of this work include the utilization of AutoML platform for hearing aid applications and the realization of AutoML model on smartphone. The experimental analysis and results demonstrate the significance and importance of using the AutoML for the current approach. The evaluations also show improvements over the state of art techniques and reflect the practical usability of the developed smartphone app in different noisy environments.

Auxiliares de Audição , Smartphone , Aprendizado de Máquina , Ruído , Inteligibilidade da Fala
J Acoust Soc Am ; 148(1): 389, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32752751


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. The proposed algorithm can be used in any stand-alone platform such as a smartphone using its two inbuilt microphones. The detailed operation of the real-time implementation on the smartphone is presented. The developed application works as an assistive tool for hearing aid devices (HADs). Speech quality and intelligibility test results are used to compare the proposed algorithm to existing conventional and neural network-based SE methods. Subjective and objective scores show the superior performance of the developed method over several conventional methods in different noise conditions and low signal to noise ratios (SNRs).

IEEE Access ; 8: 106296-106309, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32793404


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). The developed method consists of two blocks, the detector block, and the separator block. The entire setup is integrated with speech enhancement (SE) algorithms, and before the compression stage, used in a hearing aid device (HAD) signal processing pipeline. The detector recognizes the presence of alert signal in various noisy environments. The separator block separates the alert signal from the mixture of noisy signals before passing it through SE to ensure minimal or no attenuation of the alert signal. It is implemented on a smartphone as an application that seamlessly works with HADs in real-time. This smartphone assistive setup allows the hearing aid users to know the presence of the alert sounds even when these are out of sight. The algorithm is computationally efficient with a low processing delay. The key contribution of this paper includes the development and integration of alert signal separator block with SE and the realization of the entire setup on a smartphone in real-time. The proposed method is compared with several state-of-the-art techniques through objective measures in various noisy conditions. The experimental analysis demonstrates the effectiveness and practical usefulness of the developed setup in real-world noisy scenarios.