A Smart Binaural Hearing Aid Architecture Based on a Mobile Computing Platform

Li, Yingdan; Chen, Fei; Sun, Zhuoyi; Weng, Zhaoyang; Tang, Xian; Jiang, Hanjun; Wang, Zhihua

doi:10.3390/electronics8070811

Cited by 5 publications

(4 citation statements)

References 27 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%

“…Signal-to-noise ratios have improved by at least 30% in some conditions, and the total system delay was 8.8 milliseconds. Objective and subjective results show that the recommended structure, presented in [ 20 ] improves user experience.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic User Preferences Selection of Smart Hearing Aid Using BioAid

Siddiqui

Saleem

Raza

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…Both hearing aids must communicate in real time and optimize the sounds in a given situation. Mobile computing platforms [41] have improved the user experience and usability of binaural hearing aid systems. In most cases, the hearing aids in each ear communicate through a 2.45 GHz wireless communication (2.45 GHz WC) system [42][43][44].…”

Section: Proposed Hbc Systemmentioning

confidence: 99%

Transmission Analysis in Human Body Communication for Head-Mounted Wearable Devices

Muramatsu

Sasaki

2021

Electronics

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As society ages, wireless body area networks (WBANs) are expected to increasingly improve the quality of life of the elderly and disabled. One promising WBAN technology is human body communication (HBC), which utilizes part of the human body as a transmission medium. Communication between head-mounted wearable devices, such as hearing aids, is a potential HBC application. To clarify the HBC transmission mechanism between head-mounted wearable devices, this study analyzes the input impedance characteristics of the transceiver electrodes, transmission characteristics, and electric field distributions around and through a detailed head model. The investigation was performed via an electromagnetic field simulation. The signal frequency had less effect on the transmission characteristics and electric field distributions at 10, 20, and 30 MHz. However, the transmission mechanism between the head-mounted wearable devices was influenced by the number of electrodes in the transceiver. Moreover, the transmission characteristics between two-electrode transceivers were improved by impedance matching. Finally, the availability of the proposed system was evaluated from power consumption and human safety perspectives.

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“…However, its binaural implementation, as with binaural algorithms in general, requires a link between the two devices because the signals across right and left devices need to be exchanged. This can be implemented as a wired solution (which can be inconvenient to the user) or as a wireless solution, introducing a transmission delay (latency) and higher power consumption ( Dieudonné & Francart, 2018 ; Li et al., 2019 ), neither of those is a standard in the current CI technology. It is expected that binaural beamforming technology will see wider implementation in CIs, as first CI manufacturers employ low-latency wireless links (as, e.g., used in Ernst et al., 2019 ).…”

mentioning

confidence: 99%

Speech Intelligibility and Spatial Release From Masking Improvements Using Spatial Noise Reduction Algorithms in Bimodal Cochlear Implant Users

et al. 2021

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This study investigated the speech intelligibility benefit of using two different spatial noise reduction algorithms in cochlear implant (CI) users who use a hearing aid (HA) on the contralateral side (bimodal CI users). The study controlled for head movements by using head-related impulse responses to simulate a realistic cafeteria scenario and controlled for HA and CI manufacturer differences by using the master hearing aid platform (MHA) to apply both hearing loss compensation and the noise reduction algorithms (beamformers). Ten bimodal CI users with moderate to severe hearing loss contralateral to their CI participated in the study, and data from nine listeners were included in the data analysis. The beamformers evaluated were the adaptive differential microphones (ADM) implemented independently on each side of the listener and the (binaurally implemented) minimum variance distortionless response (MVDR). For frontal speech and stationary noise from either left or right, an improvement (reduction) of the speech reception threshold of 5.4 dB and 5.5 dB was observed using the ADM, and 6.4 dB and 7.0 dB using the MVDR, respectively. As expected, no improvement was observed for either algorithm for colocated speech and noise. In a 20-talker babble noise scenario, the benefit observed was 3.5 dB for ADM and 7.5 dB for MVDR. The binaural MVDR algorithm outperformed the bilaterally applied monaural ADM. These results encourage the use of beamformer algorithms such as the ADM and MVDR by bimodal CI users in everyday life scenarios.

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A Smart Binaural Hearing Aid Architecture Based on a Mobile Computing Platform

Cited by 5 publications

References 27 publications

Automatic User Preferences Selection of Smart Hearing Aid Using BioAid

Automatic User Preferences Selection of Smart Hearing Aid Using BioAid

Transmission Analysis in Human Body Communication for Head-Mounted Wearable Devices

Speech Intelligibility and Spatial Release From Masking Improvements Using Spatial Noise Reduction Algorithms in Bimodal Cochlear Implant Users

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