Novel design for non‐latency wireless binaural hearing aids

Wei, Qun; Park, Young Sik; Lee, Seul Bi; Kim, Dong Wook; Seong, Ki Woong; Lee, Jyung Hyun; Lee, Seung-Ha; Cho, Jin‐Ho

doi:10.1002/tee.22007

Cited by 5 publications

(5 citation statements)

References 2 publications

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“…The detailed parameters are summarized in Table 5. Table 6 presents the four types of HA: conventional HA [49], a wireless binaural HA [50], a smartphone-based HA system [27], and the proposed structure. Reference [49] was a mixedsignal system, and the rest are based on digital circuit design.…”

Section: Latency Complexity Analysis and Power Consumptionmentioning

confidence: 99%

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

Chen

Sun

et al. 2020

IEEE Access

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This paper presents a smartphone-based binaural hearing aid architecture for improving the speech intelligibility of hearing aid users. The proposed system consists of an earpiece, a smartphone and an application that performs real-time speech enhancement. The speaker's voice, which is picked up by the microphone of the earpiece that is worn on the ear, is transmitted to the smartphone via wireless technology. After the speech intelligibility is improved in real time by the deep learning speech enhancement application, it is returned to the earpiece and generates sound. Deep learning speech enhancement algorithms can be used without performing burdensome calculations on the processors in the hearing aid. The results showed that the average usage of the central processing unit in the smartphone was approximately 26%, and the signal-tonoise ratios improve by at least 20%. The presented objective and subjective results show that the proposed method achieves comparatively more noise suppression without distorting the audio. INDEX TERMS Binaural hearing aids, smartphone, real-time se app, e-health, deep learning.

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Section: Latency Complexity Analysis and Power Consumptionmentioning

confidence: 99%

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

Chen

Sun

et al. 2020

IEEE Access

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“…Thus, the system met the requirements for latency. Table 2 shows a single-unit-based hearing aid [34], the two wireless HA [35,36], and the proposed new structure. All the systems were digital, except that of [36], which was a mixed-signal system.…”

Section: T7: Output Buffer 2 Msmentioning

confidence: 99%

“…All the systems were digital, except that of [36], which was a mixed-signal system. Compared with [34][35][36], it is the binaural HA with the most features for speech enhancement to improve speech intelligibility. All the DSP systems were located in the earpieces, except for the proposed structure, which was in the MCP.…”

Section: T7: Output Buffer 2 Msmentioning

confidence: 99%

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

Chen

Sun

et al. 2019

Electronics

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This paper presents a new structure for hearing aids. Normally, the power consumption and user experience are contradictory. The proposed hearing aid structure mainly consists of three parts: the earpieces, the mobile computing platform, and the real-time speech-enhancement application. It can run complex algorithms without carrying out heavy calculations on the processors in the hearing aid. Thus, the binaural algorithm is utilized without being limited by complexity and power consumption to improve the user experience. Moreover, the speech-enhancement algorithm can be updated much more easily than in traditional built-in digital signal process hearing aids. A good level of user experience is achieved by combining the hearing aid and mobile computing platform with a 400-MHz transceiver; furthermore, the 400-MHz transceiver can reduce path loss around the body. The concept verification process showed that the overall usage of the central processing unit in the smartphone is around 16%, the signal-to-noise ratios show at least a 30% improvement in some environments, and the whole system delay is 8.8 ms. The presented objective and subjective results show significant improvements regarding user experience and usability brought about by the proposed structure.

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“…Note that the proposed methods start from binaural signal features, which implies that binaural rather than bilateral hearing aids are required when using these methods for sound source localization in hearing aid systems, and the issue of binaural hearing aids that need to transmit and synchronize the binaural features needed for this model is beyond the scope of this paper. Yet there is a large body of research literature that addresses this issue, and the reader is referred to Kreisman et al ( 2010 ), Ibrahim et al ( 2013 ), Wei et al ( 2014 ), and Geetha et al ( 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Toward learning robust contrastive embeddings for binaural sound source localization

Tang

Taseska

Waterschoot

2022

Front. Neuroinform.

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Recent deep neural network based methods provide accurate binaural source localization performance. These data-driven models map measured binaural cues directly to source locations hence their performance highly depend on the training data distribution. In this paper, we propose a parametric embedding that maps the binaural cues to a low-dimensional space where localization can be done with a nearest-neighbor regression. We implement the embedding using a neural network, optimized to map points that are close to each other in the latent space (the space of source azimuths or elevations) to nearby points in the embedding space, thus the Euclidean distances between the embeddings reflect their source proximities, and the structure of the embeddings forms a manifold, which provides interpretability to the embeddings. We show that the proposed embedding generalizes well in various acoustic conditions (with reverberation) different from those encountered during training, and provides better performance than unsupervised embeddings previously used for binaural localization. In addition, the proposed method performs better than or equally well as a feed-forward neural network based model that directly estimates the source locations from the binaural cues, and it has better results than the feed-forward model when a small amount of training data is used. Moreover, we also compare the proposed embedding using both supervised and weakly supervised learning, and show that in both conditions, the resulting embeddings perform similarly well, but the weakly supervised embedding allows to estimate source azimuth and elevation simultaneously.

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Novel design for non‐latency wireless binaural hearing aids

Cited by 5 publications

References 2 publications

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

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

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

Toward learning robust contrastive embeddings for binaural sound source localization

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