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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.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 968-971, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018146

RESUMO

A compressor in hearing aid devices (HADs) is responsible for mapping the dynamic range of input signals to the residual dynamic range of hearing-impaired (HI) patients. Gains and parameters of the compressor are set according to the HI patient's preferences. In different surroundings depending upon noise level, the patient may seek to tune the parameters to improve performance. Traditionally, fitting of the hearing aids is done by an audiologist using hearing aid software and the HI patient's opinion at a clinic. In this paper, we propose a frequency-based multi-band compressor implemented as a smartphone application, which can be used as an alternative to that of the traditional HADs. The proposed solution allows the user to tune the compression parameters for each band along with a choice of compression speed and fitting strategy. Exploiting smartphone processing and hardware capabilities, the application can be used for bilateral hearing loss. The performance of this easy-to-use smartphone-based application is compared with traditional HADs using a hearing aid test system. Objective and subjective evaluations are also carried out to quantify the performance.


Assuntos
Compressão de Dados , Auxiliares de Audição , Perda Auditiva , Percepção da Fala , Perda Auditiva/terapia , Perda Auditiva Bilateral , Humanos
4.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 972-975, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018147

RESUMO

Acoustic feedback cancellation is a challenging problem in the design of sound reinforcement systems, hearing aids, etc. Acoustic feedback is inevitable when the acoustic signal path forms a loop between the microphone and loudspeaker. An efficient short duration noise injection algorithm is proposed in this paper to estimate the impulse response of the acoustic feedback path model. The algorithm does not require any prior information about the acoustic feedback path. It is capable of optimally estimate the acoustic feedback path for cancellation, and avoid the occurrence of any howling episode, in varying acoustic environments. Presented algorithm is efficiently implemented on smartphone device having close proximity of loudspeaker and microphone to emulate the feedback condition. The algorithm being platform-independent can also be implemented for any set-up or system. The experimental results of the proposed method shows satisfying results and its ability to track and cancel the acoustic feedback in changing characteristics of the acoustic path.


Assuntos
Auxiliares de Audição , Ruído , Acústica , Algoritmos , Retroalimentação
5.
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).

6.
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.

7.
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.

8.
Proc Meet Acoust ; 39(1)2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-32714483

RESUMO

Multi-band Dynamic Range (MBDR) Compression is a key part of the signal processing operation in hearing aid devices (HADs). Operating speed of the MBDR compressor plays an important role in preserving the quality and intelligibility of the output signal. Traditional fast-acting compressor preserves the audible cues in quiet speech but, in presence of surrounding noise, it can degrade the sound quality by introducing pumping and breathing effects. Alternatively, slow-acting compressor maintains the temporal cues and the listening comfort but may provide inadequate gain for soft inputs that come right after loud inputs. HADs may operate in a variable acoustic environment. Therefore, a fixed speed in compression might affect the performance of the hearing aids. In this study, we propose a frequency(FFT) based nine-band adaptive MBDR compression which uses spectral flux as a measure of the intensity change in input level to adapt the speed of the compressor in each band. Gain, threshold and compression ratio of the compressor for nine bands are adjusted based on the audiogram of the hearing impaired patient. The proposed frequency-based adaptive MBDR compression method is implemented on smartphone. The objective and subjective test results demonstrate the performance of proposed method compared to fixed compression approaches.

9.
Proc Meet Acoust ; 39(1)2019 Dec 02.
Artigo em Inglês | MEDLINE | ID: mdl-32742552

RESUMO

Deep neural network (DNN) techniques are gaining popularity due to performance boost in many applications. In this work we propose a DNN-based method for finding the direction of arrival (DOA) of speech source for hearing study improvement and hearing aid applications using popular smartphone with no external components as a cost-effective stand-alone platform. We consider the DOA estimation as a classification problem and use the magnitude and phase of speech signal as a feature set for DNN training stage and obtaining appropriate model. The model is trained and derived using real speech and real noisy speech data recorded on smartphone in different noisy environments under low signal to noise ratios (SNRs). The DNN-based DOA method with the pre-trained model is implemented and run on Android smartphone in real time. The performance of proposed method is evaluated objectively and subjectively in the both training and unseen environments. The test results are presented showing the superior performance of proposed method over conventional methods.

10.
IEEE Access ; 7: 169969-169978, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-32754421

RESUMO

In this paper, we present a real-time convolutional neural network (CNN) based approach for speech source localization (SSL) using Android-based smartphone and its two built-in microphones under noisy conditions. We propose a new input feature set - using real and imaginary parts of the short-time Fourier transform (STFT) for CNN-based SSL. We use simulated noisy data from popular datasets that was augmented with few hours of real recordings collected on smartphones to train our CNN model. We compare the proposed method to recent CNN-based SSL methods that are trained on our dataset and show that our CNN-based SSL method offers higher accuracy on identical test datasets. Another unique aspect of this work is that we perform real-time inferencing of our CNN model on an Android smartphone with low latency (14 milliseconds(ms) for single frame-based estimation, 180 ms for multi frame-based estimation and frame length is 20 ms for both cases) and high accuracy (i.e. 88.83% at 0dB SNR). We show that our CNN model is rather robust to smartphone hardware mismatch, hence we may not need to retrain the entire model again for use with different smartphones. The proposed application provides a 'visual' indication of the direction of a talker on the screen of Android smartphones for improving the hearing of people with hearing disorders.

11.
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
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2018: 3549-3552, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30441145

RESUMO

Adaptive Feedback Cancellation (AFC) techniques are widely used to eliminate the undesired acoustic feedback effect arising in the Hearing Aid Devices (HADs) due to the coupling between the speaker and the microphone of the HAD. This paper proposes a method to eliminate the acoustic feedback effect in the HADs in presence of noisy environment. The method involves utilization of a computationally efficient Spectral Flux feature-based voice activity detector (VAD), which controls the process of Noise Injection in the proposed AFC algorithm (SFNIAFC). The proposed algorithm's performance is objectively evaluated using Misalignment (MISA) and Perceptual Evaluation of Speech Quality (PESQ) criteria for realistic noisy conditions. The simulations performed for the proposed method shows faster convergence and reduction in the MISA values with high PESQ values in comparison to the earlier method. Subjective test results support the effectiveness and better performance of the proposed algorithm for the HAD applications over earlier method.


Assuntos
Auxiliares de Audição , Acústica , Retroalimentação , Ruído , Processamento de Sinais Assistido por Computador , Percepção da Fala
13.
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
14.
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.

15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2016: 3674-3678, 2016 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-28269091

RESUMO

Functional Magnetic Resonance Imaging (fMRI) is used in many diagnostic procedures for neurological related disorders. Strong broadband acoustic noise generated during fMRI scan interferes with the speech communication between the physician and the patient. In this paper, we propose a single microphone Speech Enhancement (SE) technique which is based on the supervised machine learning technique and a statistical model based SE technique. The proposed algorithm is robust and computationally efficient and has capability to run in real-time. Objective and Subjective evaluations show that the proposed SE method outperforms the existing state-of-the-art algorithms in terms of quality and intelligibility of the recovered speech at low Signal to Noise Ratios (SNRs).


Assuntos
Algoritmos , Imagem por Ressonância Magnética/métodos , Inteligibilidade da Fala , Humanos , Aprendizado de Máquina , Ruído , Relações Médico-Paciente , Razão Sinal-Ruído
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2016: 5885-5888, 2016 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-28269593

RESUMO

In this paper, the development of a speech processing pipeline on smartphones for hearing aid devices (HADs) is presented. This pipeline is used for noise suppression and speech enhancement (SE) to improve speech quality and intelligibility. The proposed method is implemented to run in real-time on Android smartphones. The results of the testing conducted indicate that the proposed method suppresses the noise and improves the perceptual quality of speech in terms of three objective measures of perceptual evaluation of speech quality (PESQ), noise attenuation level (NAL), and the coherent speech intelligibility index (CSII).


Assuntos
Auxiliares de Audição , Smartphone , Percepção da Fala/fisiologia , Algoritmos , Humanos , Inteligibilidade da Fala
17.
Artigo em Inglês | MEDLINE | ID: mdl-23366520

RESUMO

Several studies on hearing impaired people who use hearing aid reveal that speech enhancement algorithms implemented in hearing aids improve listening comfort. However, these algorithms do not improve speech intelligibility too much and in many cases they decrease the speech intelligibility, both in hearing-impaired and in normally hearing people. In fact, current approaches for development of the speech enhancement algorithms (e.g. minimum mean square error (MMSE)) are not optimal for intelligibility improvement. Some recent studies investigated the effect of different distortions on the enhanced speech and realized that by controlling the amplification distortion, the intelligibility improves dramatically. In this paper, we examined, subjectively and objectively, the effects of amplification distortion on the speech enhanced by two algorithms in three background noises at different SNR levels.


Assuntos
Algoritmos , Auxiliares de Audição , Humanos
18.
Artigo em Inglês | MEDLINE | ID: mdl-23367105

RESUMO

Numerous methods have been proposed to cancel the unpleasant effects of acoustic feedback between the loudspeaker and microphone in hearing aid systems. Adaptive Feedback Cancellation (AFC) methods are often used to estimate an FIR filter for cancelling the feedback path effect. In estimating the AFC FIR filter, it is important to select the order of the filter properly; especially when the feedback path changes from one environment to another and no knowledge about it is available. Choosing improper filter order causes deficient system performance or excessive computations and power usage in the system. We present tracking of the energy of AFC FIR filters and its convergence behavior as a new criterion for determining the proper order for AFC FIR filter. Experimental results show validity of the proposed criterion.


Assuntos
Algoritmos , Auxiliares de Audição , Processamento de Sinais Assistido por Computador , Espectrografia do Som/métodos , Retroalimentação , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
19.
Artigo em Inglês | MEDLINE | ID: mdl-23367108

RESUMO

Adaptive Feedback Cancellation (AFC) methods are used to find an FIR filter to cancel the negative effect of acoustic feedback between the loudspeaker and microphone of the hearing aid. Finding the AFC filter of appropriate order/length directly affects the performance and complexity of the system. In this paper, we use noise injection method to find the AFC filter estimating the feedback path model. We show that the optimum length which guarantees a good compromise between the quality and the complexity of the system may be smaller than the length of the actual feedback path model. However, in order to improve the performance of the system in terms of Misalignment criterion, we propose using multiple short-time noise injections and averaging method to find the best filter estimate of appropriate length.


Assuntos
Algoritmos , Artefatos , Auxiliares de Audição , Processamento de Sinais Assistido por Computador , Espectrografia do Som/métodos , Retroalimentação , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Razão Sinal-Ruído
20.
IEEE Trans Biomed Eng ; 58(12): 3303-9, 2011 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-21134809

RESUMO

Functional magnetic resonance imaging (fMRI) acoustic noise exhibits an almost periodic nature (quasi-periodicity) due to the repetitive nature of currents in the gradient coils. Small changes occur in the waveform in consecutive periods due to the background noise and slow drifts in the electroacoustic transfer functions that map the gradient coil waveforms to the measured acoustic waveforms. The period depends on the number of slices per second, when echo planar imaging (EPI) sequencing is used. Linear predictability of fMRI acoustic noise has a direct effect on the performance of active noise control (ANC) systems targeted to cancel the acoustic noise. It is shown that by incorporating some samples from the previous period, very high linear prediction accuracy can be reached with a very low order predictor. This has direct implications on feedback ANC systems since their performance is governed by the predictability of the acoustic noise to be cancelled. The low complexity linear prediction of fMRI acoustic noise developed in this paper is used to derive an effective and low-cost feedback ANC system.


Assuntos
Algoritmos , Retroalimentação , Imagem por Ressonância Magnética/métodos , Processamento de Sinais Assistido por Computador , Acústica , Cabeça , Humanos , Modelos Biológicos , Ruído , Análise de Regressão , Tronco
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