A robust pathological voices recognition system based on DCNN and scattering transform

Souli, S.; Amami, Rim; Yahia, Sadok Ben

doi:10.1016/j.apacoust.2020.107854

Cited by 15 publications

(4 citation statements)

References 28 publications

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“…FIR filter is a high-pass filter, which can improve the high-frequency components. At the same time, it is also convenient for formant detection, which improves the stability of signal in quantization processing [15].…”

Section: Singing Speech Recognition and Processing Methodmentioning

confidence: 99%

Voice Quality Evaluation of Singing Art Based on 1DCNN Model

Liusong¹,

Du²

2022

Mathematical Problems in Engineering

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Traditional speech recognition still has the problems of poor robustness and low signal-to-noise ratio, which makes the accuracy of speech recognition not ideal. Combining the idea of one-dimensional convolutional neural network with objective evaluation, an improved CNN speech recognition method is proposed in this paper. The simulation experiment is carried out with MATLAB. The effectiveness and feasibility of this method are verified by simulation. This new method is based on one-dimensional convolutional neural network. The traditional 1DNN algorithm is optimized by using the fractional processing node theory, and the corresponding parameters are set. Establish an objective evaluation system based on improved 1DCNN. Through the comparison with other neural networks, the results show that the evaluation method based on the improved 1DCNN has high stability, and the error between subjective score and evaluation method is the smallest.

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Section: Singing Speech Recognition and Processing Methodmentioning

confidence: 99%

Voice Quality Evaluation of Singing Art Based on 1DCNN Model

Liusong¹,

Du²

2022

Mathematical Problems in Engineering

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“…In this paper, we used MEEI database to test the robustness of the proposed hybrid BiLMST-CNN for voice patologies detection [24,25].…”

Section: Kaypentax Databasementioning

confidence: 99%

A Robust Voice Pathology Detection System Based on the Combined BiLSTM–CNN Architecture

Amami,

Trabelsi

et al. 2023

mendel

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Voice recognition systems have become increasingly important in recent years due to the growing need for more efficient and intuitive human-machine interfaces. The use of Hybrid LSTM networks and deep learning has been very successful in improving speech detection systems. The aim of this paper is to develop a novel approach for the detection of voice pathologies using a hybrid deep learning model that combines the Bidirectional Long Short-Term Memory (BiLSTM) and the Convolutional Neural Network (CNN) architectures. The proposed model uses a combination of temporal and spectral features extracted from speech signals to detect the different types of voice pathologies. The performance of the proposed detection model is evaluated on a publicly available dataset of speech signals from individuals with various voice pathologies(MEEI database). The experimental results showed that the hybrid BiLSTM-CNN model outperforms several classifiers by achieving an accuracy of 98.86\%. The proposed model has the potential to assist health care professionals in the accurate diagnosis and treatment of voice pathologies, and improving the quality of life for affected individuals.

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“…Based on this baseline network, the first step on RestNet is a block named conv1 which involves 3 operations which are: (1) convolution using a kernel size of 7 and feature map size of 64, (2) batch normalization and (3x3) max pooling with stride 2 operation [24]. The max pooling is achieved by applying a max filter and aims to reduce the dimensions of a feature map by eliminating non-maximal components.…”

Section: I-dcnnmentioning

confidence: 99%

The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images

Saif¹,

Amami²,

Eleraky³

et al. 2022

mendel

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The new Coronavirus or simply Covid-19 causes an acute deadly disease. It has spread rapidly across the world, which has caused serious consequences for health professionals and researchers. This is due to many reasons including the lack of vaccine, shortage of testing kits and resources. Therefore, the main purpose of this study is to present an inexpensive alternative diagnostic tool for the detection of Covid-19 infection by using chest radiographs and Deep Convolutional Neural Network (DCNN) technique. In this paper, we have proposed a reliable and economical solution to detect COVID-19. This will be achieved by using X-rays of patients and an Incremental-DCNN (I-DCNN) based on ResNet-101 architecture. The datasets used in this study were collected from publicly available chest radiographs on medical repositories. The proposed I-DCNN method will help in diagnosing the positive Covid-19 patient by utilising three chest X-ray imagery groups, these will be: Covid-19, viral pneumonia, and healthy cases. Furthermore, the main contribution of this paper resides on the use of incremental learning in order to accommodate the detection system. This has high computational energy requirements, time consuming challenges, while working with large-scale and regularly evolving images. The incremental learning process will allow the recognition system to learn new datasets, while keeping the convolutional layers learned previously. The overall Covid-19 detection rate obtained using the proposed I-DCNN was of 98.70\% which undeniably can contribute effectively to the detection of COVID-19 infection.

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A robust pathological voices recognition system based on DCNN and scattering transform

Cited by 15 publications

References 28 publications

Voice Quality Evaluation of Singing Art Based on 1DCNN Model

Voice Quality Evaluation of Singing Art Based on 1DCNN Model

A Robust Voice Pathology Detection System Based on the Combined BiLSTM–CNN Architecture

The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images

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