References
- [1] C. Liu, D. Springer, Q. Li, B. Moody, R. A. Juan, F. J. Chorro, F. Castells, J. M. Roig, I. Silva, A. E. Johnson et al, “An open access database for the evaluation of heart sound algorithms”, Physiological Measurement 37 (12), (2016) 21-81.
- [2] S. Sun, Z. Jiang, H. Wang, and Y. Fang, “Automatic moment segmentation peak detection analysis of heart sound pattern via short-time modified Hilbert transform”, Computer methods programs in biomedicine 114 (3) (2014) 219-230.
- [3] Z. Yan, Z. Jiang, A. Miyamoto, and Y. Wei, “The moment segmentation analysis of heart sound pattern”, Computer methods programs in biomedicine 98 (2) (2010) 140-150.
- [4] P. Sedighian, A. W. Subudhi, F. Scalzo, and S. Asgari, “Pediatric heart sound segmentation using hidden Markov model”, Engineering in Medicine Biology Society (EMBC) 2014 36th Annual International Conference of the IEEE, IEEE, 2014,, pp. 5490-5493.
- [5] P. Bentley, G. Nordehn, M. Coimbra, S. Mannor, and R. Getz, Pascal classifying heart sounds challenge 2011 (chsc2011) results See http://www.peterjbentley.com/heartchallenge/index.html.
- [6] S. Ari, K. Hembram, and G. Saha, “Detection of cardiac abnormality from PCG signal using IMS based least fifth-square SVM classifier”, Expert Systems with Applications 37 (12) (2010) 8019-8026.
- [7] Y. Zheng, X. Guo, and X. Ding, “A novel hybrid energy fraction entropy-based approach for systolic heart murmurs identification”, Expert Systems with Applications 42 (5) (2015) 2710-721.
- [8] H. Uguz, “A biomedical system based on artificial neural network principal component analysis for diagnosis of the heart valve diseases” Journal of medical systems 36 (1) (2012) 61-72.
- [9] A. Gharehbaghi, I. Ekman, P. Ask, E. Nylander, and B. Janerot-sjoberg, “Assessment of aortic valve stenosis severity using intelligent phonocardiography” International journal of cardiology 198 (2015) 58-60.
- [10] R. Sarac Oglu, “Hiden Markov model-based classification of heart valve disease with PCA for dimension reduction” Engineering Applications of Artificial Intelligence 25 (7) (2012) 1523-1528.
- [11] A. Quiceno-Manrique, J. Godino-Llorente, M. Blanco-velasco, and G. Castellanos-Dominguez, “Selection Of Dynamic Features Based On Time-frequency Representations For Heart Murmur Detection From Phonocar-diographic Signals”, Signals of biomedical engineering 38 (1) (2010) 118-137.
- [12] N. Dehak, P. J. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, “Front-end factor analysis for speaker verification”, IEEE Transactions on Audio, Speech, Language Processing 19 (4) (2011) 788-798.
- [13] N. Dehak, P. A. Torres-carrasquillo, D. Reynolds, and R. Dehak, “Language recognition via i-vectors dimensionality reduction”, Twelfth annual conference of the international speech communication association 2011.
- [14] D. Martinez, O. Plchot, L. Burget, O. Glembek, and P. Matějka, “Language recognition in i-vectors space”, Twelfth Annual Conference of the International Speech Communicatio Association 2011.
- [15] M. H. Bahari, R. Saeidi, D. Van Leeuwen et al, “Accent recognition using i-vector Gaussian mean supervector Gaussian posterior probability supervector for spontaneous telephone speech”.
- [16] R. Xia and Y. Liu, “Using i-vector space model for emotion recognition”, Thirteenth Annual Conference of the International Speech Communication Association 2012.
- [17] H. Khaki and E. Erzin, “Continuous emotion tracking using total variability space”, Sixteenth Annual Conference of the International Speech Communication Association 2015.
- [18] H. Eghbal-Zadeh, B. Lehner, M. Dorfer, and G. Widmer, “Cp-jku submissions for dcase-2016: A hybrid approach using binaural i-vectors deep convolutional neural networks”, IEEE AASP Challenge on Detection Classification of Acoustic Scenes Events (DCASE).
- [19] M. Adiban, H. Sameti, N. Maghsoodi, and S. Shahsavari, “Sut system description for anti-spoofing 2017 challenge”, Proceedings of the 29th Conference on Computational Linguistics Speech Processing (ROCLING 2017) 2017, pp. 264-275.
- [20] R. Wahid, N. I. Ghali, H. S. Own, T. -h Kim, and A. E. Hassanien, “A Gaussian mixture models approach to human heart signal verification using different feature extraction algorithms”, Computer Applications for Bio-technology Multimedia, Ubiquitous City, Springer, 2012,, pp. 16-24.
- [21] G. D. Clifford, C. Liu, B. Moody, J. Millet, S. Schmidt, Q. Li, I. Silva, and R. G. Mark, “Recent advances in heart sound analysis”, Physiological Measurement 38 (8) (2017) E10-E25, DOI: 10, 1088/1361-6579/aa7ec8 URL https://doi.org/10.1088%2F1361-6579%2Faa7ec8.
- [22] C. Potes, S. Parvaneh, A. Rahman, and B. Conroy, “Ensemble of feature-based deep learning-based classifiers for detection of abnormal heart sounds”, 2016 Computing in Cardiology Conference (CinC) (2016) 621-624.
- [23] A. K. Dwivedi, S. A. Imtiaz, and E. Rodriguez-villegas, “Algorithms for automatic analysis classification of heart sounds a systematic review”, IEEE Access 7 (2019) 8316-8345.
- [24] F. Renna, J. Oliveira, and M. T. Coimbra, “Convolutional neural networks for heart sound segmentation”, 2018 26th European Signal Processing Conference (EUSIPCO) IEEE, 2018, pp. 757-761.
- [25] F. Noman, C.-M. Ting, S.-H. Salleh, and H. Ombao, “Short-segment heart sound classification using an ensemble of deep convolutional neural networks”, ICASSP 2019-2019 IEEE International Conference on Acoustics Speech Signal Processing (ICASSP), IEEE, 2019, pp. 1318-1322.
- [26] A. Moukadem, A. Dieterlen, N. Hueber, and C. Brandt, “Localization of heart sounds based on s-transform radial basis function neural network”, 15th Nordic-Baltic Conference on Biomedical Engineering Medical Physics (NBC 2011) Springer, 2011, pp. 168-171.
- [27] A. Castro, T. T. Vinhoza, S. S. Mattos, and M. T. Coimbra, “Heart sound segmentation of pediatric auscultations using wavelet analysis”, Engineering in Medicine Biology Society (EMBC) 2013 35th Annual International Conference of the IEEE, IEEE, 2013, pp. 3909-3912.
- [28] S. Patidar, R. B. Pachori, and N. Garg, “Automatic diagnosis of septal defects based on tunable-q wavelet transform of cardiac sound signals”, Expert Systems with Applications 42 (7) (2015) 3315-3326.
- [29] A. A. Sepehri, J. Hancq, T. Dutoit, A. Gharehbaghi, A. Kocharian, and A. Kiani, “Computerized screening of children congenital heart diseases”, Computer methods programs in biomedicine 92 (2) (2008) 186-192.
- [30] Z. Abduh, E. A. Nehary, M. A. Wahed, and Y. M. Kadah, “Classification of heart sounds using fractional Fourier transform based mel-frequency spectral coeficients stacked autoencoder deep neural network”, Journal of Medical Imaging Health Informatics 9 (1) (2019) 1-8.
- [31] S. E. Schmidt, C. Holst-Hansen, C. Graff, E. Toft, and J. J. Struijk, “Segmentation of heart sound recordings by a duration-dependent hidden Markov model”, Physiological measurement 31 (4) (2010) 513.
- [32] D. B. Springer, L. Tarassenko, and G. D. Clifford, “Logistic regression-HSMM-based heart sound segmentation”, IEEE Transactions on Biomedical Engineering 63 (4) (2016) 822-832.
- [33] H. Uguz, “Adative neuro-fuzzy inference system for diagnosis of the heart valve diseases using wavelet transform with entropy”, Neural Computing Applications 21 (7) (2012) 1617-1628.
- [34] M. R. Hasan, M. Jamil, M. Rahman et al, “Speaker identification using MEL frequency cepstral coeficients”, 1(4).
- [35] V. Tiwari, “MFCC its applications in speaker recognition”, International journal on emerging technologies 1 (1) (2010) 19-22.
- [36] H. Zeinali, A. Mirian, H. Sameti, and B. Babaali, “Non-speaker information reduction from cosine similarity scoring in i-vector based speaker verification”, Computers & Electrical Engineering 48 (2015) 226-238.
- [37] D. A. Reynolds, T. F. Quatieri, and R. B. Dunn, “Speaker verification using adapted Gaussian mixture models”, Digital signal processing 10 (1-3) (2000) 19-41.
- [38] W. M. Campbell, D. E. Sturim, D. A. Reynolds, and A. Solomonoff, “SVM based speaker verification using a GMM supervector kernel NAP variability compensation”, Acoustics, Speech Signal Processing, 2006, ICASSP 2006 Proceedings, 2006 IEEE International Conference on, vol. 1, IEEE, 2006, pp. I-I.
- [39] A. Solomonoff, C. Quillen, and W. M. Campbell, “Channel compensation for SVM speaker recognition”, Odyssey, vol. 4, Citeseer, 2004, pp. 219-226.
- [40] A. Solomonoff, W. M. Campbell, and I. Boardman, “Advances in channel compensation for SVM speaker recognition”, Acoustics Speech, Signal Processing, 2005, Pp, I-629 Proceedings, (ICASSP’05), IEEE International Conference, vol, 1, IEEE, 2005, pp, I-629,, 24.
- [41] A. O. Hatch, S. Kajarekar, and A. Stolcke, “Within-class covariance normalization for SVM-based speaker recognition”, Ninth international conference on spoken language processing 2006.
- [42] N. Dehak, P. Kenny, R. Dehak, O. Glembek, P. Dumouchel, L. Burget, V. Hubeika, and F. Castaldo, “Support vector machines joint factor analysis for speaker verification”,.
- [43] C. R. Rao, “The utilization of multiple measurements in problems of biological classification”, Journal of the Royal Statistical Society, Series B Methodological 10 (2) (1948) 159-203.
- [44] L. Van Der Maaten, E. Postma, and J. Van Den Herik, “Dimensionality reduction: A comparative”, J Mach Learn Res 10 (2009) 66-71.
- [45] L. Van Der Maaten, E. Postma, and J. Van Den Herik, “Principal component analysis”, Wiley interdisciplinary reviews: computational statistics 2 (4) (2010) 433-459.
- [46] D. Jang, H. Park, and G. Choi, “Estimation of leakage ratio using principal component analysis artificial neural network in water distribution systems”, Sustainability 10 (3) (2018) 750,.
- [47] D. P. Kingma and M. Welling, “Auto-encoding variational Bayes”, arXiv preprint arXiv:1312, 6114.
- [48] D. P. Kingma and M. Welling, “D. Reynolds Gaussian mixture models”, Encyclopedia of biometrics (2015) 827-832.
- [49] D. A. Reynolds, “Automatic speaker recognition using Gaussian mixture speaker models”, The Lincoln Laboratory Journal Citeseer, 1995.
- [50] B. Bozkurt, I. Germanakis, and Y. Stylianou, “A study of time-frequency features for CNN-based automatic heart sound classification for pathology detection”, Computers in biology medicine.