Design of Compensated Multi-Channel Dynamic-Range Compressor for Hearing Aid Devices using Polyphase Implementation

Zou, Ziyan

doi:10.1109/embc.2018.8512441

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…As Zou, Hao and Panahi show, multi-channel compression introduces distortion to the system, and increases computational complexity. To eliminate this, they investigated a compensation filter to reduce distortion [11].…”

Section: Related Workmentioning

confidence: 99%

Non-linear Distortion against Hearing Loss

et al. 2020

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Today, an increasing problem is hearing loss caused by increasingly high Sound Pressure Levels (SPL). Extremely loud noises and sounds originating from our environment often cause damage to our ears and result in noise-induced hearing loss (NIHL). There are procedures to increase loudness without increasing the physical, measurable Sound Pressure Level (SPL). Procedures using equal loudness sensitivity curves amplify at different frequencies. It was our intention to create a device that will control volume and clarity to a suitable level without inflicting any damage to the ear since the sound pressure level remains at a lower level. Additionally, our aim was to design a device, that, in contrast with current procedures today, does not weaken or masquerade the sound, does not distort the intricacies of music or alter tonality. Environmental conscious thinking, especially in energy saving, has become increasingly important today. When amplifying sound, we can achieve significant energy savings from utilising a lower Sound Pressure Level (SPL). Our patented method imitates the human ear's distortion and non-linear behaviour. Any non-linear distorting causes a sense of loudness growth, but the modified sound will become realistic if we come closer to the human ear's distortion. In our investigations, we conclude that, by setting (or "imitating") an overtone range similar to the human ear's distortion, we can achieve an increase in loudness in the entire audible frequency spectrum without loss of sound quality. Our prototype was constructed with the ability to carry out a subjective comparative loudness test. The data was collected individually by questionnaire, and later calculated using the arithmetical mean of the answers, with the results given in decibels. An average increment of 2.7 dB was measured electronically based on the 66 completed questionnaires used with the Triode Proof of Concept Model. The results demonstrated that an increase of loudness level can be achieved not only with the currently used Fletcher-Munson curves, but with the help of the procedure found in our study. In the implementation of this unique method, an increase in the sound pressure level can be avoided, and, so, without SPL increases, the subjective loudness of the sound can be increased, ensuring the ear will not be exposed to the adverse consequences of the higher sound pressure level. Our unique equipment and methodology can help reduce hearing damage without the discomfort caused by coloured tones. While further studies and improvements are required, the usability of this method demonstrates its practical applicability. These may include various electronic sound amplifiers in which the method can be used as a sound effect module. However, the most important field now using this method is huge number of media players, as young people commonly use these audio tools. The greatest benefit of this unique approach is therefore a reduction of hearing damage caused by the widespread and reckless use of headphones.

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Section: Related Workmentioning

confidence: 99%

Non-linear Distortion against Hearing Loss

et al. 2020

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Real-Time Convolutional Neural Network-Based Speech Source Localization on Smartphone

et al. 2019

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In this paper, we present a real-time convolutional neural network (CNN) based approach for speech source localization (SSL) using Android-based smartphone and its two built-in microphones under noisy conditions. We propose a new input feature set -using real and imaginary parts of the short-time Fourier transform (STFT) for CNN-based SSL. We use simulated noisy data from popular datasets that was augmented with few hours of real recordings collected on smartphones to train our CNN model. We compare the proposed method to recent CNN-based SSL methods that are trained on our dataset and show that our CNN-based SSL method offers higher accuracy on identical test datasets. Another unique aspect of this work is that we perform real-time inferencing of our CNN model on an Android smartphone with low latency (14 milliseconds(ms) for single frame-based estimation, 180 ms for multi frame-based estimation and frame length is 20 ms for both cases) and high accuracy (i.e. 88.83% at 0dB SNR). We show that our CNN model is rather robust to smartphone hardware mismatch, hence we may not need to retrain the entire model again for use with different smartphones. The proposed application provides a 'visual' indication of the direction of a talker on the screen of Android smartphones for improving the hearing of people with hearing disorders.INDEX TERMS Convolutional neural network, speech source localization (SSL), smartphone, direction of arrival (DOA).

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Design of Compensated Multi-Channel Dynamic-Range Compressor for Hearing Aid Devices using Polyphase Implementation

Cited by 2 publications

References 9 publications

Non-linear Distortion against Hearing Loss

Non-linear Distortion against Hearing Loss

Real-Time Convolutional Neural Network-Based Speech Source Localization on Smartphone

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