Two speech enhancement-based hearing aid systems and comparative study

Author(s):  
Zheng Gong ◽  
Youshen Xia
2021 ◽  
Author(s):  
Ajay S ◽  
Manisha R ◽  
Pranav Maheshkumar Nivarthi ◽  
Sai Harsha Nadendla ◽  
C Santhosh Kumar

2021 ◽  
Vol 150 (4) ◽  
pp. A348-A348
Author(s):  
Gautam Shreedhar Bhat ◽  
Nikhil Shankar ◽  
Issa Panahi

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hrishikesh B Vanjari ◽  
Mahesh T Kolte

Purpose Speech is the primary means of communication for humans. A proper functioning auditory system is needed for accurate cognition of speech. Compressed sensing (CS) is a method for simultaneous compression and sampling of a given signal. It is a novel method increasingly being used in many speech processing applications. The paper aims to use Compressive sensing algorithm for hearing aid applications to reduce surrounding noise. Design/methodology/approach In this work, the authors propose a machine learning algorithm for improving the performance of compressive sensing using a neural network. Findings The proposed solution is able to reduce the signal reconstruction time by about 21.62% and root mean square error of 43% compared to default L2 norm minimization used in CS reconstruction. This work proposes an adaptive neural network–based algorithm to enhance the compressive sensing so that it is able to reconstruct the signal in a comparatively lower time and with minimal distortion to the quality. Research limitations/implications The use of compressive sensing for speech enhancement in a hearing aid is limited due to the delay in the reconstruction of the signal. Practical implications In many digital applications, the acquired raw signals are compressed to achieve smaller size so that it becomes effective for storage and transmission. In this process, even unnecessary signals are acquired and compressed leading to inefficiency. Social implications Hearing loss is the most common sensory deficit in humans today. Worldwide, it is the second leading cause for “Years lived with Disability” the first being depression. A recent study by World health organization estimates nearly 450 million people in the world had been disabled by hearing loss, and the prevalence of hearing impairment in India is around 6.3% (63 million people suffering from significant auditory loss). Originality/value The objective is to reduce the time taken for CS reconstruction with minimal degradation to the reconstructed signal. Also, the solution must be adaptive to different characteristics of the signal and in presence of different types of noises.


Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 811
Author(s):  
Yingdan Li ◽  
Fei Chen ◽  
Zhuoyi Sun ◽  
Zhaoyang Weng ◽  
Xian Tang ◽  
...  

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.


2015 ◽  
Vol 18 (4) ◽  
pp. 663-671 ◽  
Author(s):  
K. Prajna ◽  
K. V. V. S. Reddy ◽  
G. Sasi Bhushan Rao ◽  
R. Uma Maheswari

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