A Smart Binaural Hearing Aid Architecture Leveraging a Smartphone APP With Deep-Learning Speech Enhancement

Li, Yingdan; Chen, Fei; Sun, Zhuoyi; Ji, Junyu; Jia, Wen; Wang, Zhihua

doi:10.1109/access.2020.2982212

Cited by 9 publications

(6 citation statements)

References 39 publications

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“…The structure of HAD proposed by [ 20 , 21 ] comprises primarily three components: earpieces, mobile computing platform, and real-time speech-enhancement application. Complex algorithms can be executed without straining the HAD’s processors.…”

Section: Literature Reviewmentioning

confidence: 99%

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Automatic User Preferences Selection of Smart Hearing Aid Using BioAid

Siddiqui

Saleem

Raza

et al. 2022

Sensors

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Noisy environments, changes and variations in the volume of speech, and non-face-to-face conversations impair the user experience with hearing aids. Generally, a hearing aid amplifies sounds so that a hearing-impaired person can listen, converse, and actively engage in daily activities. Presently, there are some sophisticated hearing aid algorithms available that operate on numerous frequency bands to not only amplify but also provide tuning and noise filtering to minimize background distractions. One of those is the BioAid assistive hearing system, which is an open-source, freely available downloadable app with twenty-four tuning settings. Critically, with this device, a person suffering with hearing loss must manually alter the settings/tuning of their hearing device when their surroundings and scene changes in order to attain a comfortable level of hearing. However, this manual switching among multiple tuning settings is inconvenient and cumbersome since the user is forced to switch to the state that best matches the scene every time the auditory environment changes. The goal of this study is to eliminate this manual switching and automate the BioAid with a scene classification algorithm so that the system automatically identifies the user-selected preferences based on adequate training. The aim of acoustic scene classification is to recognize the audio signature of one of the predefined scene classes that best represent the environment in which it was recorded. BioAid, an open-source biological inspired hearing aid algorithm, is used after conversion to Python. The proposed method consists of two main parts: classification of auditory scenes and selection of hearing aid tuning settings based on user experiences. The DCASE2017 dataset is utilized for scene classification. Among the many classifiers that were trained and tested, random forests have the highest accuracy of 99.7%. In the second part, clean speech audios from the LJ speech dataset are combined with scenes, and the user is asked to listen to the resulting audios and adjust the presets and subsets. A CSV file stores the selection of presets and subsets at which the user can hear clearly against the scenes. Various classifiers are trained on the dataset of user preferences. After training, clean speech audio was convolved with the scene and fed as input to the scene classifier that predicts the scene. The predicted scene was then fed as input to the preset classifier that predicts the user’s choice for preset and subset. The BioAid is automatically tuned to the predicted selection. The accuracy of random forest in the prediction of presets and subsets was 100%. This proposed approach has great potential to eliminate the tedious manual switching of hearing assistive device parameters by allowing hearing-impaired individuals to actively participate in daily life by automatically adjusting hearing aid settings based on the acoustic scene.

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Section: Literature Reviewmentioning

confidence: 99%

“…In [ 21 ], the microphone of the earpiece was used to acquire the speaker’s voice, which was then wirelessly sent to the smartphone. After the deep learning speech enhancement application improves the speech intelligibility in real time, it then returned to the earpiece to make sound.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic User Preferences Selection of Smart Hearing Aid Using BioAid

Siddiqui

Saleem

Raza

et al. 2022

Sensors

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“…The specific method is to freeze all the model parameters first. The original BN layer statistics are shown in Equation (2).…”

Section: Fast and Accurate Evaluation With Adaptive Bnmentioning

confidence: 99%

“…How to efficiently extract useful knowledge from such an amount of raw data has become a problem. Thanks to recent advances in deep learning, state-of-the-art deep learning models achieved significant performance improvements in a broad spectrum of areas with enough data, including computer vision [1], speech analysis [2], smart sensing [3], etc. However, to achieve better results, deep learning models usually have to go wider and deeper, which incurs high computational costs in terms of storage, memory, latency, and energy.…”

Section: Introductionmentioning

confidence: 99%

Combine-Net: An Improved Filter Pruning Algorithm

Wang

Zhang

2021

Information

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The powerful performance of deep learning is evident to all. With the deepening of research, neural networks have become more complex and not easily generalized to resource-constrained devices. The emergence of a series of model compression algorithms makes artificial intelligence on edge possible. Among them, structured model pruning is widely utilized because of its versatility. Structured pruning prunes the neural network itself and discards some relatively unimportant structures to compress the model’s size. However, in the previous pruning work, problems such as evaluation errors of networks, empirical determination of pruning rate, and low retraining efficiency remain. Therefore, we propose an accurate, objective, and efficient pruning algorithm—Combine-Net, introducing Adaptive BN to eliminate evaluation errors, the Kneedle algorithm to determine the pruning rate objectively, and knowledge distillation to improve the efficiency of retraining. Results show that, without precision loss, Combine-Net achieves 95% parameter compression and 83% computation compression on VGG16 on CIFAR10, 71% of parameter compression and 41% computation compression on ResNet50 on CIFAR100. Experiments on different datasets and models have proved that Combine-Net can efficiently compress the neural network’s parameters and computation.

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“…The great progress of speech enhancement researches based on deep neural network in the academic field has promoted its wide application in the industrial field [1][2][3][4]. However, for a determined real application scenario, the relevant speech enhancement methods still need to be improved and optimized according to the characteristics and needs of the scene.…”

Section: Introductionmentioning

confidence: 99%

DCCTN: Deep Complex Convolution Transformer Network for Far-field and Low SNR Speech Enhancement

Sun

Qin

2022

J. Phys.: Conf. Ser.

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Single-channel speech enhancement has made great progress with the development of deep learning. Recently, some researchers predict the real and imaginary parts of the output respectively by deep complex convolutional networks and achieve state-of-art performance. Based on this, in this paper, we design a new network structure, called Deep Complex Convolution Transformer Network (DCCTN), which is dedicated to solving the problem of single-channel speech enhancement under the conditions of far-field and extremely low signal-to-noise ratio (SNR). First, based on the Deep Complex Convolution Recurrent Network (DCCRN), a two-stage Transformer masking module is introduced to replace the recurrent network structure and better focus on the long-term correlation of speech. Secondly, the deep complex Transformer structure and gaussian weight matrix are introduced to make the model more suitable for far-field and very low SNR scenarios. In the experiment, we mixed real far-field clean speech and noise to make training and test data sets. The experimental results show that in the target scene, DCCTN proposed has significantly improved speech-enhancement performance, compared to most of the latest methods.

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A Smart Binaural Hearing Aid Architecture Leveraging a Smartphone APP With Deep-Learning Speech Enhancement

Cited by 9 publications

References 39 publications

Automatic User Preferences Selection of Smart Hearing Aid Using BioAid

Automatic User Preferences Selection of Smart Hearing Aid Using BioAid

Combine-Net: An Improved Filter Pruning Algorithm

DCCTN: Deep Complex Convolution Transformer Network for Far-field and Low SNR Speech Enhancement

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