Classification of Harmful Noise Signals for Hearing Aid Applications using Spectrogram Images and Convolutional Neural Networks

Zaman, Khalid; Şah, Melike; Direkoğlu, Cem

doi:10.1109/ismsit50672.2020.9254451

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…Moreover, this approach is chiefly utilized in bandwidth reduction functions [6]. However, numerous applications may demand a higher bandwidth than the fixed-length filter's performance [7]. However, this could result in distortion of signal response outcome [8].…”

Section: Introductionmentioning

confidence: 99%

VHDL based Design of an Efficient Hearing Aid Filter using an Intelligent Variable-Bandwidth-Filter

Rawandale¹,

Ganorkar²,

Kolte³

2023

IJACSA

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Filtering techniques have been elaborated in the HA field to improve signal clarity and enhance the hearing capacity of deaf people. However, public sounds are highly noisy, so filtering those signals is not an easy task. Hence, the present article has aimed to develop a novel Ant Lion based power Noise-Variable Bandwidth Filter (ALPN-VBF) for the HA applications. Here, the proposed optimized power efficient filter has incorporated several functions like de-noising and frequency tuning based on the word features. Here, the signal's noise has been removed with the maximum possible range with the help of High-pass-Filter (HPF) and low-pass filter (LPF). Finally, the developed model is tested with a few audiograms, and the filter parameters have been analyzed and compared with other models. The testing results have proved that the designed filter is better in frequency tuning and signal transmission than the previous approaches by attaining less delay and reduced power consumption rate.

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Section: Introductionmentioning

confidence: 99%

VHDL based Design of an Efficient Hearing Aid Filter using an Intelligent Variable-Bandwidth-Filter

Rawandale¹,

Ganorkar²,

Kolte³

2023

IJACSA

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show abstract

“…On the other hand, the feature visualization method visualizes regional features in the time domain by periodically dividing the data in it. Compared with the time and frequency domains, which express the features in one dimension, it is possible to analyze the local features in two dimensions [9,10]. Radar acquires data with relatively low noise in environments of low-light conditions, in rainy and cloudy circumstances, or other conditions which usual image sensors suffer from.…”

Section: Introductionmentioning

confidence: 99%

Classification of Respiratory States Using Spectrogram with Convolutional Neural Network

Park

Lee

2022

Applied Sciences

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This paper proposes an approach to the classification of respiration states based on a neural network model by visualizing respiratory signals using a spectrogram. The analysis and processing of human biosignals are still considered some of the most crucial and fundamental research areas in both signal processing and medical applications. Recently, learning-based algorithms in signal and image processing for medical applications have shown significant improvement from both quantitative and qualitative perspectives. Human respiration is still considered an important factor for diagnosis, and it plays a key role in preventing fatal diseases in practice. This paper chiefly deals with a contactless-based approach for the acquisition of respiration data using an ultra-wideband (UWB) radar sensor because it is simple and easy for use in an experimental setup and shows high accuracy in distance estimation. This paper proposes the classification of respiratory states by using a feature visualization scheme, a spectrogram, and a neural network model. The proposed method shows competitive and promising results in the classification of respiratory states. The experimental results also show that the method provides better accuracy (precision: 0.86 and specificity: 0.90) than conventional methods that use expensive equipment for respiration measurement.

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A Survey of Audio Classification Using Deep Learning

Zaman,

Sah,

Direkoglu

et al. 2023

IEEE Access

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Classification of Harmful Noise Signals for Hearing Aid Applications using Spectrogram Images and Convolutional Neural Networks

Cited by 4 publications

References 18 publications

VHDL based Design of an Efficient Hearing Aid Filter using an Intelligent Variable-Bandwidth-Filter

VHDL based Design of an Efficient Hearing Aid Filter using an Intelligent Variable-Bandwidth-Filter

Classification of Respiratory States Using Spectrogram with Convolutional Neural Network

A Survey of Audio Classification Using Deep Learning

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