A new speech enhancement method for two‐input two‐output hearing aids

2006 ◽  
Vol 120 (5) ◽  
pp. 3157-3157 ◽  
Author(s):  
Junfeng Li ◽  
Shuichi Sakamoto ◽  
Yo‐iti Suzuki ◽  
Satoshi Hongo
Keyword(s):  
Speech Enhancement ◽  
Hearing Aids ◽  
Enhancement Method

A Supervised Speech Enhancement Method for Smartphone-Based Binaural Hearing Aids

2020 ◽  
Vol 14 (5) ◽  
pp. 951-960
Author(s):  
Zhuoyi Sun ◽  
Yingdan Li ◽  
Hanjun Jiang ◽  
Fei Chen ◽  
Xiang Xie ◽  
...  
Keyword(s):  
Speech Enhancement ◽  
Hearing Aids ◽  
Binaural Hearing ◽  
Enhancement Method ◽  
Binaural Hearing Aids

scholarly journals Speech recognition with a hearing-aid processing scheme combining beamforming with mask-informed speech enhancement

2022 ◽  
Vol 26 ◽  
pp. 233121652110686
Author(s):  
Tim Green ◽  
Gaston Hilkhuysen ◽  
Mark Huckvale ◽  
Stuart Rosen ◽  
Mike Brookes ◽  
...  
Keyword(s):  
Speech Enhancement ◽  
Hearing Aids ◽  
Spatial Information ◽  
Signal To Noise Ratio ◽  
Target Position ◽  
Spatial Diversity ◽  
Processing Scheme ◽  
Time Frequency ◽  
Sentence Recognition ◽  
Enhancement Method

A signal processing approach combining beamforming with mask-informed speech enhancement was assessed by measuring sentence recognition in listeners with mild-to-moderate hearing impairment in adverse listening conditions that simulated the output of behind-the-ear hearing aids in a noisy classroom. Two types of beamforming were compared: binaural, with the two microphones of each aid treated as a single array, and bilateral, where independent left and right beamformers were derived. Binaural beamforming produces a narrower beam, maximising improvement in signal-to-noise ratio (SNR), but eliminates the spatial diversity that is preserved in bilateral beamforming. Each beamformer type was optimised for the true target position and implemented with and without additional speech enhancement in which spectral features extracted from the beamformer output were passed to a deep neural network trained to identify time-frequency regions dominated by target speech. Additional conditions comprising binaural beamforming combined with speech enhancement implemented using Wiener filtering or modulation-domain Kalman filtering were tested in normally-hearing (NH) listeners. Both beamformer types gave substantial improvements relative to no processing, with significantly greater benefit for binaural beamforming. Performance with additional mask-informed enhancement was poorer than with beamforming alone, for both beamformer types and both listener groups. In NH listeners the addition of mask-informed enhancement produced significantly poorer performance than both other forms of enhancement, neither of which differed from the beamformer alone. In summary, the additional improvement in SNR provided by binaural beamforming appeared to outweigh loss of spatial information, while speech understanding was not further improved by the mask-informed enhancement method implemented here.

A Speech Enhancement Method Using Attention Mechanism and Gated Recurrent Unit

2021 ◽  
Author(s):  
Kaibei Peng ◽  
Xiaoming Sun ◽  
Haowei Chen ◽  
Zhen He ◽  
Jianrong Wang
Keyword(s):  
Speech Enhancement ◽  
Attention Mechanism ◽  
Enhancement Method ◽  
Gated Recurrent Unit

An Optimization Adaptive BWT Speech Enhancement Method

2014 ◽  
Vol 13 (10) ◽  
pp. 1730-1736 ◽  
Author(s):  
Cao Bin-Fang ◽  
Li Jian-Qi ◽  
Qu Peixin ◽  
Peng Guang-Han
Keyword(s):  
Speech Enhancement ◽  
Enhancement Method

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

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 811
Author(s):  
Yingdan Li ◽  
Fei Chen ◽  
Zhuoyi Sun ◽  
Zhaoyang Weng ◽  
Xian Tang ◽  
...  
Keyword(s):  
Power Consumption ◽  
Mobile Computing ◽  
User Experience ◽  
Speech Enhancement ◽  
Hearing Aids ◽  
Hearing Aid ◽  
Binaural Hearing ◽  
The Body ◽  
Processing Unit ◽  
Computing Platform

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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