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Two-way voice feature representation for disease detection based on voice using 1D and 2D deep convolution neural network

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APPLIED ACOUSTICS 2025年 233卷

作者： Wagdarikar, Narendra Jagtap, Sonal GH Raisoni Coll Engn & Management Dept E &TC Engn Pune India Smt Kashibai Navale Coll Engn Dept Elect & Telecommun Pune India Smt Kashibai Navale Coll Engn Dept E &TC Engn Pune India

Voice pathology deals with detecting diseases with the help of the voice, as diseases significantly impact the voice. Machine learning (ML) and deep learning (DL) schemes have been presented for disease detection using voice. However, the outcomes of the system are limited due to poor spectro-temporal representation, less feature distinctiveness, low-frequency resolution problems, lower detection rates, etc. This article presents voice-based pathology using two-way voice feature representation (TWVFR), which consists of two parallel arms of a deep convolution neural network (DCNN) for feature representation. The first parallel arm considers the Mel Frequency Cepstral Coefficient Spectrogram (MFCCS), fed to 2-D DCNN to characterize the spectral domain characteristics of the voice signal. The second approach consists of multiple voice features (MVF), such as spectral domain (SD), time domain (TD), and voice quality (VQ) features. The essential features are selected using the Spider Monkey optimization algorithm and given to 1D-DCNN. The last layer features are combined and given to a fully connected layer followed by the Softmax classifier. The Softmax classifier classifies the speech signal into normal and diseased voices. The system outcomes are validated on the Saarbruecken Voice Dataset (SVD) for four class disease classifications: Bulb Paralysis, Cyste, Polyp, and Normal. The suggested TWVFR scheme provides improved overall accuracy of 98.33%, recall of 0.98, precision of 0.98, F1-score of 0.98, selectivity of 0.98, and negative predictive rate of 0.98 compared to existing methods. The TWVFR helps to enhance the feature depiction and provides an overall accuracy of 98.33% than MVF-1D DCNN (95.45%). The suggested TWVFR scheme provides improved overall accuracy of 98.33%, recall of 0.98, precision of 0.98, F1-score of 0.98, selectivity of 0.98, and negative predictive rate of 0.98 compared to existing methods.

关键词： Voice pathology deep learning deep convolution neural network Multiple voice features

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Multimodal Emotion Recognition based on Face and Speech using deep convolution neural network and Long Short Term Memory

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2025年 1-28页

作者： Taware, Shwetkranti Thakare, Anuradha D. Pimpri Chinchwad Coll Engn Dept Comp Engn Pune India

Multimodal emotion recognition (MER) is crucial for analyzing a person's mental behavior and health to enhance the performance of human-computer-interaction systems. Various deep learning-based MER systems have been presented in the last decade. However, the outcomes of the MER schemes are limited due to poor feature representation, lower correlation in short and long-term features, security issues, lower generalization capability, lower reliability of emotional modality systems, and higher computational intricacy of deep learning models. This paper presents the MER based on facial images and speech data using parallel deep convolution neural network (PDCNN) and bidirectional long short-term memory (BiLSTM) to improve the system's reliability, security, and robustness. The PDCNN aims to offer superior generalization capability and feature depiction;however, BiLSTM offers better long-term dependency, temporal representation, and correlation between the multimodal data's short and long-term attributes. The novel hybrid Particle Swarm Optimization based on Multi-Attribute Utility Theory and Archimedes Optimization Algorithm (PMA) is used to select crucial features of the facial expressions and speech data to minimize the computational intricacy of the PDCNN-LSTM framework. It offers an overall improved accuracy of 99.22%, precision of 0.9967, recall of 0.9933, and F1-score of 0.9949 for MER on the BAUM dataset compared to traditional techniques.

关键词： Affective computing Multimodal emotion recognition deep convolution neural network Local binary pattern deep learning Particle swarm optimization

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Predictive machine health monitoring using deep convolution neural network for noisy vibration signal of rotating machine using empirical mode decomposition

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DISCOVER APPLIED SCIENCES 2025年第4期7卷

作者： Pavithra, R. Ramachandran, Prakash Vellore Inst Technol Sch Elect Engn Vellore Tamil Nadu India

In a noisy industry environment, to predict machine faults using vibration signals, a specially designed deep convolution neural network (DCNN) with an additional noisy layer has been recently demonstrated. On the contrary, this paper presents a noise-susceptible fault classification using frequency spectrums in standard DCNN. The study involves two types of spectrum representation (a) Short-time Fourier Transform (STFT)of raw original signal and (b) Hilbert Huang Transform (HHT) of Empirical mode decomposition-intrinsic mode function (EMD-IMFs) and two different datasets (i) CWRU Bearing dataset and (ii) Nasa Milling Dataset which is non-stationary. Three binary DCNN classification problems are performed. For bearing data both representations maintained 100% classification accuracies for noise range up to 10 dB. HHT-EMD-IMF performs better for the non-stationary milling dataset. HHT-EMD-IMF is extended to bearing data set multiclass fault classification problem. The performance is compared with state-of-the-art work. The study compares all IMFs of clean and noisy signals to quantify the impact of noise on EMD for 8 different specific faults of the CWRU bearing dataset. The analysis of average normalized noise shows that EMD-IMF0 has minimum deviation due to noise for all noise ranges. The DCNN model maintains 100% classification accuracy at high noise levels up to 10 dB and is better than the state-of-the-art noisy CNN approach. An ablation study shows that the proposed method is highly susceptible to impulse noise as well. It is also shown that the proposed method does not need additional computation time for training as in noisy layer CNN.

关键词： Empirical mode decomposition-intrinsic mode function deep convolution neural network Hilbert Huang Transform Root means square

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deep convolution neural network based Parkinson's disease detection using line spectral frequency spectrum of running speech

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期45卷 4599-4615页

作者： Kumari, Rani Ramachandran, Prakash Vellore Inst Technol Sch Elect Engn Vellore Tamil Nadu India

The deformation of speech caused by glottic vocal tract is an early bio marker for Parkinson's disease. A novel idea of Line Spectral Frequency trajectory spectrum image representation of the speech signals of the subjects in deep convolution neural network is proposed for Parkinson's disease classification in which the convolution layer automatically learn the features from the input images and no separate feature calculation stage in required. The human vocal tract that produces a short phonetics is assumed as an all-pole Infinite impulse response system and the Line spectral frequency trajectory spectrum images represents the poles of the system and reflects the voice defects due to Parkinson's disease. It is shown that the proposed method outperforms the existing state of the art work for two different utterance tasks one for sustained phonation and another for natural running speech dataset. It is demonstrated that the deep convolution neural network results in a training accuracy of 92.5% for sustained phonation dataset and training accuracy of 99.18% for King's college running speech dataset. The validation accuracies for both the datasets are 100%. The proposed work is much better than another recent benchmark work in which Mel Frequency Cepstral Coefficient parameters are used in machine learning for Parkinson's disease detection in running speech. The high performance of the proposed method for King's college running speech dataset which is collected through mobile device voice recordings, gains attention. Rigorous performance analysis is performed for running speech dataset by using separate isolated test set for repeated 50 trials and the performance metrics are F1 score of 99.37%, sensitivity of 100%, precision of 98.75% and specificity of 99.27%.

关键词： deep convolution neural network line spectral frequency Parkinson's disease running speech sustained phonation

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deep convolution neural network Discriminator for Distinguishing Seborrheic Keratosis and Flat Warts 15

Deep Convolution Neural Network Discriminator for Distinguis...

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15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)

作者： Guo, Kehua Li, Ting Huang, Runhe Kang, Jian Cent South Univ Sch Informat Sci & Engn Changsha Peoples R China Hosei Univ Fac Comp & Informat Sci Tokyo Japan Cent South Univ Xiangya Sch Med Changsha Peoples R China

ISBN: (纸本)9781538619568

Seborrheic keratosis (SK) and flat warts (FW) are two common occurrences of skin disease, both of which are clinically difficult to identify. However, the treatment and prognosis of the two diseases are very different, so whether they can be accurately distinguished by dermatologists is significant and important in clinical, and it helps to provide reliable and accurate decision-making for better treatment. This paper presents a deep convolution neural network discriminator for distinguishing SK and FW. The SK and FW discriminator (SFD) aims to identify and diagnose the confocal laser scanning microscope images of SK and FW by deep convolution neural network. The experimental results show that SFD performs almost equally well compared with individual dermatologists, the discriminator can be used to identify and diagnose between SK and FW.

关键词： deep convolution neural network distinguishing diagnose

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Traffic Anomaly Detection in Wireless Sensor networks Based on Principal Component Analysis and deep convolution neural network

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IEEE ACCESS 2022年 10卷 103136-103149页

作者： Yao, Chengpeng Yang, Yu Yin, Kun Yang, Jinwei Engn Univ PAP Coll Informat Engn Xian 710086 Peoples R China

With the popularity of wireless networks, wireless sensor networks (WSNs) have advanced rapidly, and their flexibility and ease of deployment have resulted in more security concerns, making it critical to research network intrusion prevention for WSNs. Denial of service (DoS) is a common network attack, achieving its goal by bringing down the target network. A DoS attack on WSNs devices with limited resources would be fatal. This paper proposes a method based on principal component analysis (PCA) and a deep convolution neural network (DCNN) for DoS traffic anomaly detection in WSNs, based on the vulnerability of WSNs to attacks and the limited storage space of their devices. Compared with the conventional deep learning structure, the proposed model has a lightweight structure and more effective feature extraction capability, which can effectively detect network abnormal traffic in WSNs devices with limited storage capacity. To assure the effectiveness of the proposed model, receiver operating characteristic (ROC) curves, various classification metrics, and confusion matrices are used to verify the classification results of the model. Through experimental comparison, the proposed model, with small model size, outperforms other mainstream abnormal traffic detection models in terms of classification effect.

关键词： Wireless sensor networks Feature extraction Anomaly detection Principal component analysis Telecommunication traffic convolutional neural networks Representation learning Wireless sensor networks denial of service network attack principal component analysis deep convolution neural network

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A Novel Face Recognition Algorithm based on the deep convolution neural network and Key Points Detection Jointed Local Binary Pattern Methodology

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2017年第1期12卷 363-372页

作者： Huang, Wen-zhun Zhang, Shan-wen Xijing Univ Sch Informat Engn Xian Peoples R China

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.

关键词： Face recognition deep convolution neural network Key points detection Local binary pattern Feature extraction Optimization

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Emotional design of bamboo chair based on deep convolution neural network and deep convolution generative adversarial network

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第2期44卷 1977-1989页

作者： Kang, Xinhui Nagasawa, Shin'ya Wu, Yixiang Xiong, Xingfu Nanchang Univ Sch Architecture & Design 999 Xuefu Ave Nanchang 330031 Jiangxi Peoples R China Waseda Univ Grad Sch Commerce Tokyo Japan Yiwu Ind & Commercial Coll Yiwu Peoples R China

Bamboo furniture is made of green and environmentally friendly bamboo, there is a unique hand temperature and weaving beauty in addition to bamboo texture and characteristics. In the past, making bamboo furniture relied on the traditional experience of craftsmen, which had less change in appearance and lack of communication with customers, and could not meet the fashion and aesthetic needs of modern people. Therefore, this paper connects deep convolution neural network (DCNN) and deep convolution generative adversarial network (DCGAN) to generate bamboo furniture design that meets customers' emotional needs. First, based on collecting 17856 bamboo furniture in the market, DCNN builds product image recognition models and enhances image recognition performance, thereby optimizing computational efficiency and obtaining high-quality output. The optimal recognition rate of emotional data set throughout the chair product is 98.7%, of which the modern chair has a recognition rate of 99.2%, and the recognition rate of fashion bamboo chairs is 98.2%. Second, DCGAN learns a good intermediate feature from a large quantity of non-marked images and automatically generates product styling that arouses the emotional resonance of customers. Finally, the fashion designers use this creative picture as the source of inspiration, cooperate with individual characteristics and trends of the times, then design green sustainable bamboo chairs. These design plans have increased the variety of product modalities, which greatly enhances customers' emotional satisfaction and increases product sales. The collaborative design method proposed in this paper provides new ideas for generating the emotional design of bamboo furniture, which can also expand to other industrial product designs.

关键词： Emotional design artificial intelligence deep convolution generative adversarial networks deep convolution neural network bamboo furniture

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Enhanced sentiment labeling and implicit aspect identification by integration of deep convolution neural network and sequential algorithm

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CLUSTER COMPUTING-THE JOURNAL OF networkS SOFTWARE TOOLS AND APPLICATIONS 2019年第3-Sup期22卷 S5839-S5857页

作者： Feng, Jinzhan Cai, Shuqin Ma, Xiaomeng Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China China Merchants Bank Post Doctoral Sci Res Workstn Shenzhen 518040 Guangdong Peoples R China

The existing methods for Chinese sentiment Labeling mainly relies on the artificial sentiment corpus, but a sentiment word in the corpus may not be sentiment words in different sentences. This paper proposes a new method to label the words in the sentences by combining deep convolution neural network with sequential algorithm., We first extract the aspects comprised by words vectors, part of speech vectors, dependent syntax vectors to train the deep convolution neural network, and then employ the sequential algorithm to obtain the sentiment annotation of the sentence. Experimental results verify that our method is effective for sentiment labeling. Considering that the identification of the implicit aspects can improve the completeness of sentiment analysis, we suggest to construct the tuples including aspect, sentiment shifter, sentiment intensity, sentiment words after obtaining the sentiment labels for each word in the sentence. We develop new algorithm for implicit aspect identification by taking the two key factors of the aspects as a topic and the match degree of aspects and sentiment words, and the human language habit. The experiment demonstrates that the algorithm can effectively identify the implicit aspect. In this paper, we solve the problem of sentiment labeling and implicit aspect recognition in sentiment analysis. As a new tool for sentiment analysis, our method can be applied to the enterprise management information analysis, such as product online review, product online reputation, brand image and consumer preference management, and can also be used for the sentiment analysis of large-scale text data.

关键词： Sentiment labeling Implicit aspect deep convolution neural network Sequential algorithm Sentiment analysis deep learning

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Remaining Useful Life Estimation of Aircraft Engines Based on deep convolution neural network and LightGBM Combination Model

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2021年第1期14卷 1页

作者： Liu, Lijun Wang, Lan Yu, Zhen Xiamen Univ Sch Aerosp Engn Xiamen Peoples R China Xiamen Univ Shenzhen Res Inst Shenzhen Peoples R China

Accurately predicting the remaining useful life (RUL) of aero-engines is of great significance for improving the reliability and safety of aero-engine systems. Because of the high dimension and complex features of sensor data in RUL prediction, this paper proposes a model combining deep convolution neural networks (DCNN) and the light gradient boosting machine (LightGBM) algorithm to estimate the RUL. Compared with traditional prognostics and health management (PHM) techniques, signal processing of raw sensor data and prior expertise are not required. The procedure is shown as follows. First, the time window of raw data of the aero-engine is used as the input of DCNN after normalization. The role of DCNN is to extract information from the input data. Second, considering the limitations of the fully connected layer of DCNN, we replace it with a strong classifier-LightGBM to improve the accuracy of prediction. Finally, to prove the effectiveness of the proposed method, we conducted some experiments on the C-MAPSS data set provided by NASA, and obtained good accuracy. By comparing the prediction effect with other commonly used algorithms on the same data set, the proposed algorithm has obvious advantages.

关键词： deep convolution neural network Light gradient boosting machine Aero-engine Remaining useful life prediction

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