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1.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 952-955, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018142

RESUMO

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.


Assuntos
Auxiliares de Audição , Smartphone , Voz , Humanos , Ruído , Inteligibilidade da Fala
2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 956-959, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018143

RESUMO

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.


Assuntos
Auxiliares de Audição , Smartphone , Aprendizado de Máquina , Ruído , Inteligibilidade da Fala
3.
J Acoust Soc Am ; 148(1): 389, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32752751

RESUMO

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).

4.
IEEE Access ; 8: 106296-106309, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32793404

RESUMO

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.

5.
IEEE Access ; 7: 78421-78433, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-32661495

RESUMO

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. A multi-objective learning architecture including primary and secondary features uses a mapping-based convolutional neural network (CNN) model to remove noise from a noisy speech spectrum. The algorithm is computationally fast and has a low processing delay which enables it to operate seamlessly on a smartphone. The steps and the detailed analysis of real-time implementation are discussed. The proposed method is compared with existing conventional and neural network-based SE techniques through speech quality and intelligibility metrics in various noisy speech conditions. The key contribution of this paper includes the realization of CNN SE model on a smartphone processor that works seamlessly with HA. The experimental results demonstrate significant improvements over the state-of-the-art techniques and reflect the usability of the developed SE application in noisy environments.

6.
Proc Meet Acoust ; 39(1)2019 Dec 02.
Artigo em Inglês | MEDLINE | ID: mdl-33005287

RESUMO

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 in real-time to separate speech and noise in realistic scenarios for Hearing-Aid (HA) applications. The experimental results using objective and subjective tests reveal the usefulness of the developed method for real-world applications.

7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2018: 417-420, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30440422

RESUMO

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. The proposed SE gain is based on the Log-Spectral Amplitude estimator to improve the speech quality in the presence of different background noises. Objective evaluation and intelligibility measures support the theoretical analysis and show significant improvements of the proposed method in comparison with existing methods. Subjective test results show the effectiveness of the application in real-world noisy conditions at SNR levels of -5 dB, 0 dB, and 5 dB.


Assuntos
Algoritmos , Auxiliares de Audição , Smartphone , Software , Humanos , Ruído , Equipamentos de Autoajuda , Razão Sinal-Ruído , Inteligibilidade da Fala , Percepção da Fala
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2018: 5503-5506, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30441583

RESUMO

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.


Assuntos
Auxiliares de Audição , Smartphone , Percepção da Fala , Ruído , Inteligibilidade da Fala
9.
Health Innov Point Care Conf ; 2017: 32-35, 2017 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-32705090

RESUMO

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. The formant frequency information avails us to control the amount of speech distortion in these frequency bands, thereby controlling speech distortion. We introduce a 'scaling' parameter for the SE gain function, which controls the gains over the non-formant frequency band, allowing the HA users to customize the playback speech using a smartphone application to their listening preference. Objective intelligibility measures show the effectiveness of the proposed SE method. Subjective results reflect the suitability of the developed Speech Enhancement application in real-world noisy conditions at SNR levels of -5 dB, 0 dB and 5 dB.

10.
IEEE Signal Process Lett ; 24(11): 1601-1605, 2017 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-29353988

RESUMO

In this letter, we derive a new super Gaussian Joint Maximum a Posteriori (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 "tradeoff" 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. Objective quality and intelligibility measures show the effectiveness of the proposed method in comparison to benchmark techniques considered in this paper. Subjective results reflect the usefulness of the developed Speech Enhancement application in real-world noisy conditions at signal to noise ratio levels of -5 dB, 0 dB and 5 dB.

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