Enhanced smart hearing aid using deep neural networks (original) (raw)
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Environmental Noise Adaptable Hearing Aid using Deep Learning
The International Arab Journal of Information Technology
Speech de-nosing is one of the essential processes done inside hearing aids, and has recently shown a great improvement when applied using deep learning. However, when performing the speech de-noising for hearing aids, adding noise frequency classification stage is of a great importance, because of the different hearing loss types. Patients who suffer from sensorineural hearing loss have lower ability to hear specific range of frequencies over the others, so treating all the noise environments similarly will result in unsatisfying performance. In this paper, the idea of environmental adaptable hearing aid will be introduced. A hearing aid that can be programmed to multiply the background noise by a weight based on its frequency and importance, to match the case and needs of each patient. Furthermore, a more generalized Deep Neural Network (DNN) for speech enhancement will be presented, by training the network on a diversity of languages, instead of only the target language. The resu...
RETRACTED: Speech enhancement method using deep learning approach for hearing-impaired listeners
Health informatics journal, 2021
A deep learning-based speech enhancement method is proposed to aid hearing-impaired listeners by improving speech intelligibility. The algorithm decomposes the noisy speech signal into frames (as features). Subsequently, a deep convolutional neural network is fed with decomposed noisy speech signal frames to produce frequency channel estimation. However, a higher signal-to-noise ratio information is contained in produced frequency channel estimation. Using this estimate, speech-dominated cochlear implant channels are taken to produce electrical stimulation. This process is the same as that of the conventional n-of-m cochlear implant coding strategies. To determine the speech-in-noise performance of 12 cochlear implant users, the fan and music sound applied are considered as background noises. Performance of the proposed algorithm is evaluated by considering these background noises. Low processing delay and reliable architecture are the best characteristics of the deep learning-based...
IRJET- A Deep Learning Approach to Speech Enhancement for Hearing Aids
IRJET, 2021
Speech is the main source of communication in humans. Understanding speech in noisy environments is one of the major challenges in impaired people. Deep neural networks have been useful in solving standard problems in several fields including speech. They have been used to improve the speech quality for hearing impaired people. The main objective is to intensify the speech signals in terms of its nature and comprehensibility that have been manipulated by unwanted noise. This paper makes use of convolution neural networks for the enhancement of speech signals manipulated by real world noise.
Sound Noise Reduction Based on Deep Neural Networks
International Journal of Scientific Research in Science, Engineering and Technology, 2023
Audio transmittance is a generation that is now rapidly growing as a connectivity option for everyone around the world, demanding to experience the frictionless transfer of audio messages. Audio transmittance has a wide range of capabilities compared to other connectivity technologies. But we are living in the noisy world, hence while transmitting audio signal; we don’t only transmit audio, different types of noise gets transmitted with our audio signal as well which will lead to an unclear communication The basic purpose of this model is specifically focused on detecting and restoring noisy audio signals which consists various background noise. The removal of noise from the audio signal will enhance the information carrying capacity of the signal during audio communication. For the removal of noise from audio signal, a stacked Long Short Term Memory (LSTM) model is proposed. ‘Edinburgh DataShare’ dataset has been used to train the model. During the evaluation of model, the Huber loss of 0.0205 has been evaluated in 50 epochs which shows that the LSTM network was successfully implemented for noise removal of audio signal. Hence on the basis of result, we can conclude that that Stacked LSTM network works well in noise removal of audio signals
Journal of the Acoustical Society of America, 2020
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). V
Speech Enhancement Using Deep Neural Network
2016
Speech is the main source of human interaction. In everyday life,Speech understanding in noisy environments is still one of the major challenges for users. The quality and intelligibility of speech signals are generally gets corrupted by the surrounding background noise during communication. So to improve the quality and intelligibility, Corrupted speech signals is to be enhanced. In the field of speech processing, different effort has been taken to develop speech enhancement techniques in order to enhance the speech signal by reducing the amount of noise. Speech enhancement deals with improving the quality and intelligibility of speech which gets degraded in the presence of surrounding background noise. In various everyday environments, the goal of speech enhancement methods is to improving the quality and intelligibility of speech especially at low Signal-to-Noise ratios (SNR). Regarding intelligibility, different machine learning methods that aim to estimate an ideal binary mask ...
Speech Enhancement Using Deep Learning Methods: A Review
Jurnal Elektronika dan Telekomunikasi
Speech enhancement, which aims to recover the clean speech of the corrupted signal, plays an important role in the digital speech signal processing. According to the type of degradation and noise in the speech signal, approaches to speech enhancement vary. Thus, the research topic remains challenging in practice, specifically when dealing with highly non-stationary noise and reverberation. Recent advance of deep learning technologies has provided great support for the progress in speech enhancement research field. Deep learning has been known to outperform the statistical model used in the conventional speech enhancement. Hence, it deserves a dedicated survey. In this review, we described the advantages and disadvantages of recent deep learning approaches. We also discussed challenges and trends of this field. From the reviewed works, we concluded that the trend of the deep learning architecture has shifted from the standard deep neural network (DNN) to convolutional neural network ...
Ear and hearing, 2018
We investigate the clinical effectiveness of a novel deep learning-based noise reduction (NR) approach under noisy conditions with challenging noise types at low signal to noise ratio (SNR) levels for Mandarin-speaking cochlear implant (CI) recipients. The deep learning-based NR approach used in this study consists of two modules: noise classifier (NC) and deep denoising autoencoder (DDAE), thus termed (NC + DDAE). In a series of comprehensive experiments, we conduct qualitative and quantitative analyses on the NC module and the overall NC + DDAE approach. Moreover, we evaluate the speech recognition performance of the NC + DDAE NR and classical single-microphone NR approaches for Mandarin-speaking CI recipients under different noisy conditions. The testing set contains Mandarin sentences corrupted by two types of maskers, two-talker babble noise, and a construction jackhammer noise, at 0 and 5 dB SNR levels. Two conventional NR techniques and the proposed deep learning-based approa...
The Performance of Wearable Speech Enhancement System Under Noisy Environment: An Experimental Study
IEEE Access
Wearable speech enhancement can improve the recognition accuracy of the speech signals in stationary noise environments at 0dB to 60dB signal to noise ratio. Beamforming, adaptive noise reduction, and voice activity detection algorithms are used in wearable speech enhancement systems to enhance speech signals. In recent works, a word rate recognition accuracy of 63% for a 0db signal-to-noise ratio is not satisfactory for a robust speech recognition system. This paper discusses the experimental study using fixed beamforming, adaptive noise reduction, and voice activity detection algorithms with the inclusion of −10dB to 20dB signal to noise ratio for different types of noises to test the wearable speech enhancement system's performance in noisy environments. It also compares deep learning-based noise reduction methods as a benchmark for speech enhancement and word recognition for different noise levels. We have obtained an average word rate recognition accuracy of 5.74% at −10dB and 93.79% at 20dB for non-stationary noisy environments. The outcome of the experiments shows that the selected methods perform significantly better in the environment with high noise dB for both stationary and non-stationary noise. We found that there is no significant statistical difference between the stationary and non-stationary noise word recognition and SNRs level. However, the deep learning-based method performs significantly better than the fixed beamforming, adaptive noise reduction, and voice activity detection algorithms in all noisy levels. INDEX TERMS Wearable speech enhancement, beamforming, adaptive noise reduction, voice activity detection, deep learning.