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CardioXNet: A Novel Lightweight CRNN Framework for Classifying Cardiovascular Diseases from Phonocardiogram Recordings [article]

Samiul Based Shuvo, Shams Nafisa Ali, Soham Irtiza Swapnil
2020 arXiv   pre-print
stenosis, mitral regurgitation and mitral valve prolapse using raw PCG signal.  ...  For resolving this issue, in this paper, we introduce CardioXNet,a novel lightweight CRNN architecture for automatic detection of five classes of cardiac auscultation namely normal, aortic stenosis, mitral  ...  Another work proposing a novel deep WaveNet architecture has achieved a training accuracy of 97% and validation accuracy of 90% for 5 class CVD classification [19] .  ... 
arXiv:2010.01392v1 fatcat:7w3mv5mkozadjnbgjbtpsxdmtq

CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings

Samiul Based Shuvo, Shams Nafisa Ali, Soham Irtiza Swapnil, Mabrook S. Al-Rakhami, Abdu Gumaei
2021 IEEE Access  
INDEX TERMS Phonocardiogram analysis, unsegmented heart sound, cardiovascular disease, lightweight CRNN architecture, deep learning, SqueezeNet.  ...  This model outperforms any previous works using the same database by a considerable margin.  ...  Another work proposing a novel deep WaveNet architecture has achieved a training accuracy of 97% and validation accuracy of 90% for 5 class CVD classification [19] .Furthermore, two recent studies on  ... 
doi:10.1109/access.2021.3063129 fatcat:io5rva7lnnay5hkuft6sllqkz4

Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Wei Chen, Qiang Sun, Xiaomin Chen, Gangcai Xie, Huiqun Wu, Chen Xu
2021 Entropy  
The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs).  ...  With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis  ...  [37] proposed a novel 1D deep CNN for PCG patch classification.  ... 
doi:10.3390/e23060667 pmid:34073201 fatcat:q7l4nz3wcnhz3lpk6uvhonepa4

Deep generative models for musical audio synthesis [article]

M. Huzaifah, L. Wyse
2020 arXiv   pre-print
This paper is a review of developments in deep learning that are changing the practice of sound modelling.  ...  There are a few distinct approaches that have been developed historically including modelling the physics of sound production and propagation, assembling signal generating and processing elements to capture  ...  Acknowledgements This research was supported by a Singapore MOE Tier 2 grant, "Learning Generative Recurrent Neural Networks," and by an NVIDIA Corporation Academic Programs GPU grant.  ... 
arXiv:2006.06426v2 fatcat:swt7npt3gnbj5ppzcf2ef3rose

Feature-Based Fusion Using CNN for Lung and Heart Sound Classification

Zeenat Tariq, Sayed Khushal Shah, Yugyung Lee
2022 Sensors  
In this paper, we propose a novel feature-based fusion network called FDC-FS for classifying heart and lung sounds.  ...  Lung or heart sound classification is challenging due to the complex nature of audio data, its dynamic properties of time, and frequency domains.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22041521 pmid:35214424 pmcid:PMC8875944 fatcat:ubb3zmzv5zbazpfgk5ykwur3fq

Deep Learning Algorithm for Heart Valve Diseases Assisted Diagnosis

Santiago Isaac Flores-Alonso, Blanca Tovar-Corona, René Luna-García
2022 Applied Sciences  
Heart sounds are mainly the expressions of the opening and closing of the heart valves.  ...  The present work describes the design and implementation based on deep neural networks and deep learning for the binary and multiclass classification of four common valvular pathologies and normal heart  ...  Acknowledgments: We thank CONACyT for partial support of the present work. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app12083780 fatcat:7p6vbghkqrcmhlopcwf7ukbkva

Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time-Frequency Representation of PCG Recordings

Samit Kumar Ghosh, R. N. Ponnalagu, R. K. Tripathy, U. Rajendra Acharya, Chang Tang
2020 BioMed Research International  
In this paper, a time-frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals.  ...  The DLKSRN formulated using the hidden layers of extreme learning machine- (ELM-) autoencoders and kernel sparse representation (KSR) is used for the classification of PCG recordings as normal, and pathology  ...  [79] have proposed a waveNet-based DNN model for the classification of HVAs using PCG recordings and obtained an overall accuracy value of 98.20%.  ... 
doi:10.1155/2020/8843963 pmid:33415163 pmcid:PMC7769642 fatcat:lda65v5og5bzbn54da7bj35eou

A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions [article]

Shulei Ji, Jing Luo, Xinyu Yang
2020 arXiv   pre-print
The utilization of deep learning techniques in generating various contents (such as image, text, etc.) has become a trend.  ...  This paper attempts to provide an overview of various composition tasks under different music generation levels, covering most of the currently popular music generation tasks using deep learning.  ...  While sound is a set of continuous and concrete signal form encoding all the details we can hear.  ... 
arXiv:2011.06801v1 fatcat:cixou3d2jzertlcpb7kb5x5ery

Unsupervised Audiovisual Synthesis via Exemplar Autoencoders [article]

Kangle Deng and Aayush Bansal and Deva Ramanan
2021 arXiv   pre-print
We use Exemplar Autoencoders to learn the voice, stylistic prosody, and visual appearance of a specific target exemplar speech.  ...  In contrast to existing methods, the proposed approach can be easily extended to an arbitrarily large number of speakers and styles using only 3 minutes of target audio-video data, without requiring any  ...  F.4 ABLATION ANALYSIS OF WAVENET We adopt a neural-net vocoder to convert Mel-spectrograms back to raw audio signal.  ... 
arXiv:2001.04463v3 fatcat:ef7dbok5bjhn3or4bj5d45rtre

Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach

Juan P. Dominguez-Morales, Qian Liu, Robert James, Daniel Gutierrez-Galan, Angel Jimenez-Fernandez, Simon Davidson, Steve Furber
2018 2018 International Joint Conference on Neural Networks (IJCNN)  
A novel spiking neural network model has been proposed to adapt the network that has been trained with static images to a non-static processing approach, making it possible to classify audio signals and  ...  In this approach, a spiking convolutional neural network model was implemented, in which the weights of connections were calculated by training a convolutional neural network with specific activation functions  ...  The work of Juan P. Dominguez-Morales was supported by a Formación de Personal Universitario Scholarship from the Spanish Ministry of Education, Culture and Sport. Juan P.  ... 
doi:10.1109/ijcnn.2018.8489381 dblp:conf/ijcnn/Dominguez-Morales18 fatcat:sealvghhm5fn3fkhf3ajdvixqu

End-to-end heart sound segmentation using deep convolutional recurrent network

Yao Chen, Yanan Sun, Jiancheng Lv, Bijue Jia, Xiaoming Huang
2021 Complex & Intelligent Systems  
AbstractHeart sound segmentation (HSS) aims to detect the four stages (first sound, systole, second heart sound and diastole) from a heart cycle in a phonocardiogram (PCG), which is an essential step in  ...  This paper presents a novel end-to-end method based on convolutional long short-term memory (CLSTM), which directly uses audio recording as input to address HSS tasks.  ...  Dilated convolution Dilated convolution, which is achieved by the traditional operation with holes, has been previously used in various contexts, e.g., signal processing [1] , waveNet [46] , sound classification  ... 
doi:10.1007/s40747-021-00325-w fatcat:iegd7mpcvbdv3il7peficf2v6u

Three recent trends in Paralinguistics on the way to omniscient machine intelligence

Björn W. Schuller, Yue Zhang, Felix Weninger
2018 Journal on Multimodal User Interfaces  
Smallworld modelling in combination with unsupervised learning help to rapidly identify potential target data of interest.  ...  A 2 year-old has approximately heard a 1000 h of speech-at the age of ten, around ten thousand. Similarly, automatic speech recognisers are often trained on data in these dimensions.  ...  In this vein, deep learning based generative models for speech synthesis such as WaveNet [57] could be highly promising, as these allow the learning of generative models conditioned on-in principle-arbitrary  ... 
doi:10.1007/s12193-018-0270-6 fatcat:cqlvp4ozmbe6zponscyplel45i

Conditional GAN for timeseries generation [article]

Kaleb E Smith, Anthony O Smith
2020 arXiv   pre-print
Modeling synthetic data using a Generative Adversarial Network (GAN) has been at the heart of providing a viable solution.  ...  Our results demonstrate that TSGAN performs better than the competition both quantitatively using the Frechet Inception Score (FID) metric, and qualitatively when classification is used as the evaluation  ...  The most well-known deep autoregressive model in literature would be WaveNet [3] , which was inspired by PixelCNN [4] .  ... 
arXiv:2006.16477v1 fatcat:mqiyx4dgdrftrh3eu5wy6cuayq

2021 Index IEEE Transactions on Instrumentation and Measurement Vol. 70

2021 IEEE Transactions on Instrumentation and Measurement  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, and article number.  ...  ., +, TIM 2021 4002912 Heart Sounds Classification Based on Feature Fusion Using Lightweight Neural Networks.  ...  ., +, TIM 2021 5011508 Heart Sounds Classification Based on Feature Fusion Using Lightweight Neural Networks.  ... 
doi:10.1109/tim.2022.3156705 fatcat:dmqderzenrcopoyipv3v4vh4ry

16th Sound and Music Computing Conference SMC 2019 (28–31 May 2019, Malaga, Spain)

Lorenzo J. Tardón, Isabel Barbancho, Ana M. Barbancho, Alberto Peinado, Stefania Serafin, Federico Avanzini
2019 Applied Sciences  
The SMC 2019 TOPICS OF INTEREST included a wide selection of topics related to acoustics, psychoacoustics, music, technology for music, audio analysis, musicology, sonification, music games, machine learning  ...  The First International Day of Women in Inclusive Engineering, Sound and Music Computing Research (WiSMC 2019) took place on 28 May 2019.  ...  Acknowledgments: The 16th Sound and Music Computing Conference (SMC 2019) was made possible thanks to the hard work of many people including the authors, the reviewers, all the members of the Conference  ... 
doi:10.3390/app9122492 fatcat:tcacoupffjewnpjhpw4oy7x6h4
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