Multiple voice disorders in the same individual: Investigating handcrafted features, multi-label classification algorithms, and base-learners
Non-invasive acoustic analyses of voice disorders have been at the forefront of current biomedical research. Usual strategies, essentially based on machine learning (ML) algorithms, commonly classify a subject as being either healthy or pathologically-affected. Nevertheless, the latter state is not...
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| Published in: | Speech communication Vol. 152; p. 102952 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.07.2023
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| ISSN: | 0167-6393, 1872-7182 |
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| Abstract | Non-invasive acoustic analyses of voice disorders have been at the forefront of current biomedical research. Usual strategies, essentially based on machine learning (ML) algorithms, commonly classify a subject as being either healthy or pathologically-affected. Nevertheless, the latter state is not always a result of a sole laryngeal issue, i.e., multiple disorders might exist, demanding multi-label classification procedures for effective diagnoses. Consequently, the objective of this paper is to investigate the application of five multi-label classification methods based on problem transformation to play the role of base-learners, i.e., Label Powerset, Binary Relevance, Nested Stacking, Classifier Chains, and Dependent Binary Relevance with Random Forest (RF) and Support Vector Machine (SVM), in addition to a Deep Neural Network (DNN) from an algorithm adaptation method, to detect multiple voice disorders, i.e., Dysphonia, Laryngitis, Reinke’s Edema, Vox Senilis, and Central Laryngeal Motion Disorder. Receiving as input three handcrafted features, i.e., signal energy (SE), zero-crossing rates (ZCRs), and signal entropy (SH), which allow for interpretable descriptors in terms of speech analysis, production, and perception, we observed that the DNN-based approach powered with SE-based feature vectors presented the best values of F1-score among the tested methods, i.e., 0.943, as the averaged value from all the balancing scenarios, under Saarbrücken Voice Database (SVD) and considering 20% of balancing rate with Synthetic Minority Over-sampling Technique (SMOTE). Finally, our findings of most false negatives for laryngitis may explain the reason why its detection is a serious issue in speech technology. The results we report provide an original contribution, allowing for the consistent detection of multiple speech pathologies and advancing the state-of-the-art in the field of handcrafted acoustic-based non-invasive diagnosis of voice disorders.
•The proposed approach detects multiple voice disorders in one individual.•Energy, zero-crossing rates, and entropy were successfully used as features.•Over 90% of accuracy was obtained under SVD database using SMOTE. |
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| AbstractList | Non-invasive acoustic analyses of voice disorders have been at the forefront of current biomedical research. Usual strategies, essentially based on machine learning (ML) algorithms, commonly classify a subject as being either healthy or pathologically-affected. Nevertheless, the latter state is not always a result of a sole laryngeal issue, i.e., multiple disorders might exist, demanding multi-label classification procedures for effective diagnoses. Consequently, the objective of this paper is to investigate the application of five multi-label classification methods based on problem transformation to play the role of base-learners, i.e., Label Powerset, Binary Relevance, Nested Stacking, Classifier Chains, and Dependent Binary Relevance with Random Forest (RF) and Support Vector Machine (SVM), in addition to a Deep Neural Network (DNN) from an algorithm adaptation method, to detect multiple voice disorders, i.e., Dysphonia, Laryngitis, Reinke’s Edema, Vox Senilis, and Central Laryngeal Motion Disorder. Receiving as input three handcrafted features, i.e., signal energy (SE), zero-crossing rates (ZCRs), and signal entropy (SH), which allow for interpretable descriptors in terms of speech analysis, production, and perception, we observed that the DNN-based approach powered with SE-based feature vectors presented the best values of F1-score among the tested methods, i.e., 0.943, as the averaged value from all the balancing scenarios, under Saarbrücken Voice Database (SVD) and considering 20% of balancing rate with Synthetic Minority Over-sampling Technique (SMOTE). Finally, our findings of most false negatives for laryngitis may explain the reason why its detection is a serious issue in speech technology. The results we report provide an original contribution, allowing for the consistent detection of multiple speech pathologies and advancing the state-of-the-art in the field of handcrafted acoustic-based non-invasive diagnosis of voice disorders.
•The proposed approach detects multiple voice disorders in one individual.•Energy, zero-crossing rates, and entropy were successfully used as features.•Over 90% of accuracy was obtained under SVD database using SMOTE. |
| ArticleNumber | 102952 |
| Author | Aguiar, Gabriel Jonas Barbon, Sylvio Guido, Rodrigo Capobianco Patil, Hemant A. Santana, Everton José Proença, Mario Lemes |
| Author_xml | – sequence: 1 givenname: Sylvio surname: Barbon fullname: Barbon, Sylvio organization: Department of Engineering and Architecture, University of Trieste, Piazzale Europa, 1 - 34127, Trieste FVG, Italy – sequence: 2 givenname: Rodrigo Capobianco orcidid: 0000-0002-0924-8024 surname: Guido fullname: Guido, Rodrigo Capobianco email: guido@ieee.org organization: Instituto de Biociências, Letras e Ciências Exatas, Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265, Jd Nazareth, 15054-000, São José do Rio Preto SP, Brazil – sequence: 3 givenname: Gabriel Jonas surname: Aguiar fullname: Aguiar, Gabriel Jonas organization: Computer Science Department, Londrina State University, Rodovia Celso Garcia Cid/PR 445, km 380, Campus Universitário, Zip code: 86057-970, Londrina PR, Brazil – sequence: 4 givenname: Everton José surname: Santana fullname: Santana, Everton José organization: Computer Science Department, Londrina State University, Rodovia Celso Garcia Cid/PR 445, km 380, Campus Universitário, Zip code: 86057-970, Londrina PR, Brazil – sequence: 5 givenname: Mario Lemes surname: Proença fullname: Proença, Mario Lemes organization: Computer Science Department, Londrina State University, Rodovia Celso Garcia Cid/PR 445, km 380, Campus Universitário, Zip code: 86057-970, Londrina PR, Brazil – sequence: 6 givenname: Hemant A. surname: Patil fullname: Patil, Hemant A. organization: Speech Research Lab, Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT), Gandhinagar 382007, India |
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| Cites_doi | 10.1016/j.jvoice.2015.08.010 10.1145/3077136.3080834 10.32614/RJ-2018-041 10.1016/j.bspc.2014.02.001 10.1016/j.cmpb.2018.12.019 10.1016/j.ipm.2018.01.002 10.1109/TKDE.2006.162 10.1016/j.compbiomed.2015.07.017 10.1121/1.1906358 10.1016/j.eswa.2016.12.035 10.1016/j.sigpro.2012.05.028 10.1016/j.bspc.2018.01.007 10.1007/978-3-540-24775-3_5 10.1016/j.neucom.2015.02.085 10.1109/APSIPA.2017.8282229 10.1016/j.cmpb.2017.11.010 10.1016/j.artmed.2013.02.001 10.1016/j.compeleceng.2016.08.021 10.1109/MEMB.2009.934248 10.1109/ACCESS.2017.2696056 10.1109/ICASSP.2004.1325955 10.1016/j.artmed.2018.09.004 10.1016/S0167-6393(99)00080-1 10.1016/j.inffus.2010.12.001 10.1109/JBHI.2015.2467375 10.1016/j.eswa.2014.08.036 10.1016/j.bspc.2007.05.003 10.1016/j.knosys.2016.05.011 10.1016/j.compbiomed.2015.07.026 10.1016/j.sigpro.2015.08.019 10.1109/89.905995 10.1016/j.ins.2016.08.077 10.1016/j.patcog.2016.02.013 10.1016/j.ymeth.2018.07.007 10.1109/PROC.1966.4841 10.1109/ACCESS.2018.2816338 10.1016/j.inffus.2017.09.006 10.1109/CIST.2014.7016648 10.1109/KBEI.2015.7436106 10.1109/TASL.2010.2104141 10.1016/S0169-2607(01)00161-4 10.1016/j.jvoice.2012.05.002 10.1109/PGEC.1965.264137 10.1016/j.compbiomed.2013.03.006 10.1016/j.neucom.2015.12.012 10.1016/j.bspc.2018.12.024 10.1109/ICACC.2013.37 10.1016/j.neucom.2018.08.053 10.1016/j.jvoice.2018.07.014 10.1016/j.bspc.2013.11.002 10.1016/j.artmed.2015.07.005 10.1121/1.4934731 10.1039/c3mb25466f 10.4018/jdwm.2007070101 10.1016/j.cmpb.2018.10.017 10.1016/j.cmpb.2019.06.006 10.1016/j.bspc.2018.08.037 10.1007/978-3-030-00350-0_13 10.1613/jair.953 10.1016/j.compbiomed.2017.07.006 10.1109/JIOT.2020.2983911 10.1007/s10994-011-5256-5 10.1016/j.patcog.2014.09.018 10.1023/A:1010933404324 10.1007/s00500-020-04866-z 10.1016/j.patcog.2013.09.029 10.1145/1007730.1007735 10.1007/s11265-018-1376-5 |
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| Keywords | Deep learning Multiple voice disorders Handcrafted feature extraction Multi-label classification |
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| References | Liu, Chen (b43) 2015; 42 Read (b61) 2011; 85 Casper, Leonard (b16) 2011 Zhang, Zhou (b85) 2006; 18 Lachhab, O., et al., 2014. Improving the recognition of pathological voice using the discriminant HLDA Transformation. In: 3rd IEEE International Coloquium in Information Science and Technology. CIST, pp. 370–373. Batista, Prati, Monard (b13) 2004; 6 Ali (b4) 2016; 30 Wosiak, Glinka, Zakrzewska (b81) 2018; 100 Godbole, S., Sarawagi, S., 2004. Discriminative Methods for Multi-Labeled Classification. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Sidney, Australia. pp. 22–30. Guido (b32) 2018; 41 Lee (b39) 2013; 58 Rivolli, Carvalho (b62) 2018; 10 Al-Naheri (b2) 2017; 6 Verde, De Pietro, Sannino (b76) 2018; 6 Belhaj, Bouzid, Ellouze (b14) 2015; 10 Muhammad (b54) 2012; 26 Tsoumakas, Vlahavas (b74) 2007 David (b22) 2018; 154 Markaki, Stylianou (b46) 2011; 19 Mekyska (b49) 2015; 167 Xia, Xu (b82) 2012; 13 Mastelini (b48) 2019; 91 Misra, H., 2004. Spectral entropy based feature for robust ASR. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, QB, Canada. p. 193. Muhammad, Melhem (b52) 2014; 11 Schroeder (b67) 1966; 54 Babatsouli (b11) 2016; 4 Chollet (b18) 2018 Guido (b30) 2016; 179 Licklider (b41) 1948; 20 Saarela, Ryynanen, Ayramo (b63) 2019; 95 Ankıshan (b8) 2019; 48 Gómez-García, Moro-Velázquez, Godino-Llorente (b29) 2019; 51 Hegde (b34) 2019; 33 Wang, Bi, Zhang (b80) 2020; 7 Lenc, Kral (b40) 2016 Martinez (b47) 2012 Muhammad (b53) 2012; 26 Tsoumakas, Katakis, Vlahavas (b73) 2009 Pereira (b56) 2018; 54 Almeida (b5) 2018; 320 Orozco-Arroyave (b55) 2015; 19 Senge, R., et al., 2013. Rectifying classifier chains for multi-label classification. In: Proceedings Workshop LWA, Lernen-Wissensentdeckung-Adaptivitat, Bamberg, Germany. pp. 151–158. Georgoulas (b26) 2007; 2 Barry, Putzer (b12) 2007 Vinay, Bharathi (b79) 2019; 6 Cover (b19) 1965 Zhang, Mei, Chen, Li (b84) 2016; 56 Saeedi, Almasganj (b64) 2013; 43 Areiza-Laverde, H.J., Castro-Ospina, A.E., Peluffo-Ordonez, D.H., 2018. Voice pathology detection using artificial neural networks and support vector machines powered by a multicriteria optimization algorithm. In: International Workshop on Experimental and Efficient Algorithms, L’Aquila, Italy. pp. 148–159. Ghasem (b27) 2019; 177 Zarinbal, Zarandia, Turksen (b83) 2015; 48 Akbari, Arjmandi (b1) 2014; 10 Zhau, Hansen, Kaiser (b86) 2001 Zufferey (b88) 2015; 65 Arji (b10) 2019; 168 Liu, J., et al., 2017. Deep learning for extreme multi-label text classification. In: Proc. of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Tokyo, Japan. pp. 115–124. Al-Nasheri (b3) 2018; 6 Ji (b36) 2020; 24 Techakesari, Ford (b71) 2013; 93 Rallapalli, Alexander (b60) 2015; 138 Hemmerling, Skalski, Gajda (b35) 2016; 69 Krawczyk, Schaefer, Wozniak (b37) 2015; 65 Lin (b42) 2013; 9 Lorenzo, Claudia (b45) 2002; 68 Pranav, Sabarimalai (b58) 2017; 22 Guido (b31) 2016; 105 Zhong (b87) 2016; 126 Chawla (b17) 2002; 16 AlRshoud (b6) 2019; 171 Amami, Smiti (b7) 2017; 57 Montanes (b51) 2014; 47 Quatieri (b59) 2008 Haixiang (b33) 2017; 73 Tsoumakas, Katakis (b72) 2007; 3 Breiman (b15) 2001; 45 Sasou, A., 2017. Automatic identification of pathological voice quality based on the GRBAS categorization. In: Asia-Pacific and Information Processing Association Annual Summit and Conference. APSIPA ASC, Malaysia, pp. 1243–1247. Cummins, Baird, Schuller (b21) 2018; 151 Shilaskar, Ghatol, Chatur (b69) 2017; 384 Sorower (b70) 2010 Fonseca, Pereira (b25) 2008; 28 de Carvalho, Freitas (b23) 2009 Salehi, P., 2015. Using patient’s speech signal for vocal ford disorders detection based on lifting scheme. In: IEEE 2nd International Conference on Knowledge-Based Engineering and Innovation. KBEI, Tehran, Iran, pp. 561–568. Crammer, Singer (b20) 2003; 3 Vikram, C.M., Umarani, K., 2013. Phoneme independent pathological voice detection using wavelet bases, MFCCs and GMM-SVM hybrid classifier. In: International Conference on Advances in Computing, Communications and Informatics. ICACCI, Chengdu, China, pp. 153–156. Vapnik (b75) 1995 Doddington (b24) 2000 Potharaju, Sreedevi (b57) 2016; 9 Verde, Pietro, Sannino (b77) 2018; 42 Pereira (10.1016/j.specom.2023.102952_b56) 2018; 54 Barry (10.1016/j.specom.2023.102952_b12) 2007 Ankıshan (10.1016/j.specom.2023.102952_b8) 2019; 48 Liu (10.1016/j.specom.2023.102952_b43) 2015; 42 Gómez-García (10.1016/j.specom.2023.102952_b29) 2019; 51 Al-Naheri (10.1016/j.specom.2023.102952_b2) 2017; 6 Rivolli (10.1016/j.specom.2023.102952_b62) 2018; 10 Zhau (10.1016/j.specom.2023.102952_b86) 2001 Zufferey (10.1016/j.specom.2023.102952_b88) 2015; 65 Guido (10.1016/j.specom.2023.102952_b30) 2016; 179 10.1016/j.specom.2023.102952_b38 Quatieri (10.1016/j.specom.2023.102952_b59) 2008 Tsoumakas (10.1016/j.specom.2023.102952_b73) 2009 10.1016/j.specom.2023.102952_b78 Lenc (10.1016/j.specom.2023.102952_b40) 2016 Tsoumakas (10.1016/j.specom.2023.102952_b72) 2007; 3 10.1016/j.specom.2023.102952_b9 Xia (10.1016/j.specom.2023.102952_b82) 2012; 13 Casper (10.1016/j.specom.2023.102952_b16) 2011 Ghasem (10.1016/j.specom.2023.102952_b27) 2019; 177 AlRshoud (10.1016/j.specom.2023.102952_b6) 2019; 171 Chawla (10.1016/j.specom.2023.102952_b17) 2002; 16 Haixiang (10.1016/j.specom.2023.102952_b33) 2017; 73 Crammer (10.1016/j.specom.2023.102952_b20) 2003; 3 David (10.1016/j.specom.2023.102952_b22) 2018; 154 Lorenzo (10.1016/j.specom.2023.102952_b45) 2002; 68 Licklider (10.1016/j.specom.2023.102952_b41) 1948; 20 Breiman (10.1016/j.specom.2023.102952_b15) 2001; 45 Lin (10.1016/j.specom.2023.102952_b42) 2013; 9 de Carvalho (10.1016/j.specom.2023.102952_b23) 2009 Muhammad (10.1016/j.specom.2023.102952_b54) 2012; 26 Belhaj (10.1016/j.specom.2023.102952_b14) 2015; 10 Lee (10.1016/j.specom.2023.102952_b39) 2013; 58 10.1016/j.specom.2023.102952_b28 Read (10.1016/j.specom.2023.102952_b61) 2011; 85 Ali (10.1016/j.specom.2023.102952_b4) 2016; 30 Hegde (10.1016/j.specom.2023.102952_b34) 2019; 33 Markaki (10.1016/j.specom.2023.102952_b46) 2011; 19 Potharaju (10.1016/j.specom.2023.102952_b57) 2016; 9 10.1016/j.specom.2023.102952_b68 10.1016/j.specom.2023.102952_b66 Babatsouli (10.1016/j.specom.2023.102952_b11) 2016; 4 Techakesari (10.1016/j.specom.2023.102952_b71) 2013; 93 Amami (10.1016/j.specom.2023.102952_b7) 2017; 57 Ji (10.1016/j.specom.2023.102952_b36) 2020; 24 Guido (10.1016/j.specom.2023.102952_b32) 2018; 41 Wang (10.1016/j.specom.2023.102952_b80) 2020; 7 Guido (10.1016/j.specom.2023.102952_b31) 2016; 105 Pranav (10.1016/j.specom.2023.102952_b58) 2017; 22 Zhang (10.1016/j.specom.2023.102952_b85) 2006; 18 Almeida (10.1016/j.specom.2023.102952_b5) 2018; 320 Martinez (10.1016/j.specom.2023.102952_b47) 2012 Muhammad (10.1016/j.specom.2023.102952_b53) 2012; 26 Hemmerling (10.1016/j.specom.2023.102952_b35) 2016; 69 10.1016/j.specom.2023.102952_b65 Schroeder (10.1016/j.specom.2023.102952_b67) 1966; 54 Akbari (10.1016/j.specom.2023.102952_b1) 2014; 10 Doddington (10.1016/j.specom.2023.102952_b24) 2000 Saeedi (10.1016/j.specom.2023.102952_b64) 2013; 43 Krawczyk (10.1016/j.specom.2023.102952_b37) 2015; 65 Vinay (10.1016/j.specom.2023.102952_b79) 2019; 6 Wosiak (10.1016/j.specom.2023.102952_b81) 2018; 100 Verde (10.1016/j.specom.2023.102952_b77) 2018; 42 Cover (10.1016/j.specom.2023.102952_b19) 1965 Georgoulas (10.1016/j.specom.2023.102952_b26) 2007; 2 Sorower (10.1016/j.specom.2023.102952_b70) 2010 Muhammad (10.1016/j.specom.2023.102952_b52) 2014; 11 Zhang (10.1016/j.specom.2023.102952_b84) 2016; 56 Batista (10.1016/j.specom.2023.102952_b13) 2004; 6 Montanes (10.1016/j.specom.2023.102952_b51) 2014; 47 Al-Nasheri (10.1016/j.specom.2023.102952_b3) 2018; 6 Orozco-Arroyave (10.1016/j.specom.2023.102952_b55) 2015; 19 Zarinbal (10.1016/j.specom.2023.102952_b83) 2015; 48 Shilaskar (10.1016/j.specom.2023.102952_b69) 2017; 384 Fonseca (10.1016/j.specom.2023.102952_b25) 2008; 28 Saarela (10.1016/j.specom.2023.102952_b63) 2019; 95 Mastelini (10.1016/j.specom.2023.102952_b48) 2019; 91 10.1016/j.specom.2023.102952_b50 Chollet (10.1016/j.specom.2023.102952_b18) 2018 Mekyska (10.1016/j.specom.2023.102952_b49) 2015; 167 Vapnik (10.1016/j.specom.2023.102952_b75) 1995 Tsoumakas (10.1016/j.specom.2023.102952_b74) 2007 10.1016/j.specom.2023.102952_b44 Cummins (10.1016/j.specom.2023.102952_b21) 2018; 151 Rallapalli (10.1016/j.specom.2023.102952_b60) 2015; 138 Arji (10.1016/j.specom.2023.102952_b10) 2019; 168 Verde (10.1016/j.specom.2023.102952_b76) 2018; 6 Zhong (10.1016/j.specom.2023.102952_b87) 2016; 126 |
| References_xml | – start-page: 201 year: 2001 end-page: 216 ident: b86 article-title: Non-linear feature based classification of speech under stress publication-title: IEEE Trans. Speech Audio Process. – volume: 10 start-page: 24 year: 2018 end-page: 37 ident: b62 article-title: The utiml package: Multi-label classification in R publication-title: R J. – volume: 54 start-page: 720 year: 1966 end-page: 734 ident: b67 article-title: Vocoders: Analysis and synthesis of speech publication-title: Proc. IEEE – volume: 6 start-page: 20 year: 2004 end-page: 29 ident: b13 article-title: A study of the behavior of several methods for balancing machine learning training data publication-title: ACM SIGKDD Explor. Newsl. – volume: 10 start-page: 495 year: 2015 ident: b14 article-title: Edema and nodule pathological voice identification by SVM classifier on speech signal publication-title: Comput. Softw. – volume: 3 start-page: 1 year: 2007 end-page: 13 ident: b72 article-title: Multi-label classification: An overview publication-title: Int. J. Data Warehous. Min. (IJDWM) – volume: 167 start-page: 94 year: 2015 end-page: 111 ident: b49 article-title: Robust and complex approach of pathological speech signal analysis publication-title: Neurocomputing – start-page: 667 year: 2009 end-page: 685 ident: b73 article-title: Mining multi-label data publication-title: Data Mining and Knowledge Discovery Handbook – volume: 42 start-page: 134 year: 2018 end-page: 144 ident: b77 article-title: A methodology for voice classification based on the personalized fundamental frequency estimation publication-title: Biomed. Signal Process. Control – start-page: 177 year: 2009 end-page: 195 ident: b23 article-title: A tutorial on multi-label classification techniques publication-title: Found. Comput. Intell. – volume: 20 start-page: 150 year: 1948 end-page: 159 ident: b41 article-title: The influence of interaural phase relations upon the masking of speech by white noise publication-title: J. Acoust. Soc. Am. – volume: 54 start-page: 359 year: 2018 end-page: 369 ident: b56 article-title: Correlation analysis of performance measures for multi-label classification publication-title: Inf. Process. Manage. – volume: 65 start-page: 34 year: 2015 end-page: 43 ident: b88 article-title: Performance comparison of multi-label learning algorithms on clinical data for chronic diseases publication-title: Comput. Biol. Med. – volume: 177 start-page: 277 year: 2019 end-page: 283 ident: b27 article-title: Diagnosis of autism spectrum disorder based on complex network features publication-title: Comput. Methods Programs Biomed. – volume: 105 start-page: 248 year: 2016 end-page: 269 ident: b31 article-title: ZCR-aided neurocomputing: A study with applications publication-title: Knowl.-Based Syst. – volume: 11 start-page: 1 year: 2014 end-page: 9 ident: b52 article-title: Pathological voice detection and binary classification using MPEG-7 audio features publication-title: Biomed. Signal Process. Control – start-page: 225 year: 2000 end-page: 254 ident: b24 article-title: The NIST speaker recognition evaluation: Overview, methodology, systems, results, perspective publication-title: Speech Commun. – reference: Misra, H., 2004. Spectral entropy based feature for robust ASR. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, QB, Canada. p. 193. – volume: 16 start-page: 321 year: 2002 end-page: 357 ident: b17 article-title: Smote: Synthetic minority over-sampling technique publication-title: J. Artificial Intelligence Res. – reference: Salehi, P., 2015. Using patient’s speech signal for vocal ford disorders detection based on lifting scheme. In: IEEE 2nd International Conference on Knowledge-Based Engineering and Innovation. KBEI, Tehran, Iran, pp. 561–568. – volume: 6 start-page: 6969 year: 2017 end-page: 6974 ident: b2 article-title: Voice pathology detection and classification using auto-correlation and entropy features in different frequency regions publication-title: IEEE Access – volume: 13 start-page: 31 year: 2012 end-page: 47 ident: b82 article-title: Entropy/cross entropy-based group decision making under intuitionistic fuzzy environment publication-title: Inform. Fusion – volume: 168 start-page: 39 year: 2019 end-page: 57 ident: b10 article-title: A systematic literature review and classification of knowledge discovery in traditional medicine publication-title: Comput. Methods Programs Biomed. – year: 2008 ident: b59 article-title: Discrete-Time Speech Signal Processing: Principles and Practice – volume: 33 start-page: 947.e11 year: 2019 end-page: 947.e33 ident: b34 article-title: A survey on machine learning approaches for automatic detection of voice disorders publication-title: J. Voice – year: 1995 ident: b75 article-title: The Nature of Statistical Learning Theory – volume: 30 start-page: 757.e7 year: 2016 end-page: 757.e19 ident: b4 article-title: Automatic voice pathology detection with running speech by using estimation of auditory spectrum and cepstral coefficients based on the all-pole model publication-title: J. Voice – volume: 69 start-page: 270 year: 2016 end-page: 276 ident: b35 article-title: Voice data mining for laryngeal pathology assessment publication-title: Comput. Biol. Med. – volume: 138 start-page: 3061 year: 2015 end-page: 3072 ident: b60 article-title: Neural-scaled entropy predicts the effects of nonlinear frequency compression on speech perception publication-title: J. Acoust. Soc. Am. – start-page: 406 year: 2007 end-page: 417 ident: b74 article-title: Random k-labelsets: An ensemble method for multilabel classification publication-title: European Conference on Machine Learning – volume: 6 start-page: 16246 year: 2018 end-page: 16255 ident: b76 article-title: Voice disorder identification by using machine learning techniques publication-title: IEEE Access – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: b15 article-title: Random forests publication-title: Mach. Learn. – reference: Liu, J., et al., 2017. Deep learning for extreme multi-label text classification. In: Proc. of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Tokyo, Japan. pp. 115–124. – volume: 56 start-page: 1 year: 2016 end-page: 15 ident: b84 article-title: Feature selection in mixed data: A method using a novel fuzzy rough set-based information entropy publication-title: Pattern Recognit. – reference: Areiza-Laverde, H.J., Castro-Ospina, A.E., Peluffo-Ordonez, D.H., 2018. Voice pathology detection using artificial neural networks and support vector machines powered by a multicriteria optimization algorithm. In: International Workshop on Experimental and Efficient Algorithms, L’Aquila, Italy. pp. 148–159. – volume: 41 start-page: 161 year: 2018 end-page: 175 ident: b32 article-title: A tutorial-review on entropy-based handcrafted feature extraction for information fusion publication-title: Inf. Fusion – reference: Godbole, S., Sarawagi, S., 2004. Discriminative Methods for Multi-Labeled Classification. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Sidney, Australia. pp. 22–30. – volume: 179 start-page: 264 year: 2016 end-page: 282 ident: b30 article-title: A tutorial on signal energy and its applications publication-title: Neurocomputing – start-page: 460 year: 2016 end-page: 471 ident: b40 article-title: Deep neural networks for czech multi-label document classification publication-title: International Conference on Intelligent Text Processing and Computational Linguistics – year: 2007 ident: b12 article-title: Saarbrücken Voice Database – volume: 68 start-page: 135 year: 2002 end-page: 145 ident: b45 article-title: Software corrections of vocal disorders publication-title: Comput. Methods Programs Biomed. – reference: Senge, R., et al., 2013. Rectifying classifier chains for multi-label classification. In: Proceedings Workshop LWA, Lernen-Wissensentdeckung-Adaptivitat, Bamberg, Germany. pp. 151–158. – volume: 100 start-page: 279 year: 2018 end-page: 288 ident: b81 article-title: Multi-label classification methods for improving comorbidities identification publication-title: Comput. Biol. Med. – start-page: 25 year: 2010 ident: b70 article-title: A Literature Survey on Algorithms for Multi-Label Learning, Vol. 18, no. 1 – year: 2011 ident: b16 article-title: Understanding Voice Problems: A Physiological Perspective for Diagnosis and Treatment – volume: 65 start-page: 219 year: 2015 end-page: 227 ident: b37 article-title: A hybrid cost-sensitive ensemble for imbalanced breast thermogram classification publication-title: Artif. Intell. Med. – volume: 42 start-page: 1083 year: 2015 end-page: 1093 ident: b43 article-title: A multi-label classification based approach for sentiment classification publication-title: Expert Syst. Appl. – volume: 91 start-page: 191 year: 2019 end-page: 215 ident: b48 article-title: Multi-output tree chaining: An interpretative modelling and lightweight multi-target approach publication-title: J. Signal Process. Syst. – volume: 171 start-page: 9 year: 2019 ident: b6 article-title: Implementation of voice pathology detection system using feature selection publication-title: Comput. Methods Programs Biomed. – volume: 3 start-page: 1025 year: 2003 end-page: 1058 ident: b20 article-title: A family of additive online algorithms for category ranking publication-title: J. Mach. Learn. Res. – start-page: 99 year: 2012 end-page: 109 ident: b47 article-title: Voice pathology detection on the Saarbrücken voice database with calibration and fusion of scores using multifocal toolkit publication-title: Advances in Speech and Language Technologies for Iberian Languages – reference: Lachhab, O., et al., 2014. Improving the recognition of pathological voice using the discriminant HLDA Transformation. In: 3rd IEEE International Coloquium in Information Science and Technology. CIST, pp. 370–373. – volume: 24 start-page: 15327 year: 2020 end-page: 15340 ident: b36 article-title: Multi-label learning for crop leaf diseases recognition and severity estimation based on convolutional neural networks publication-title: Soft Comput. – volume: 58 start-page: 51 year: 2013 end-page: 61 ident: b39 article-title: Prediction of body mass index status from voice signals based on machine learning for automated medical applications publication-title: Artif. Intell. Med. – volume: 2 start-page: 69 year: 2007 end-page: 79 ident: b26 article-title: Novel approach for fetal heart rate classification introducing grammatical evolution publication-title: Biomed. Signal Process. Control – volume: 85 start-page: 333 year: 2011 ident: b61 article-title: Classifier chains for multi-label classification publication-title: Mach. Learn. – volume: 320 start-page: 35 year: 2018 end-page: 46 ident: b5 article-title: Applying multi-label techniques in emotion identification of short texts publication-title: Neurocomputing – volume: 9 start-page: 1 year: 2016 end-page: 7 ident: b57 article-title: An improved prediction of kidney disease using smote publication-title: Indian J. Sci. Technol. – volume: 19 start-page: 1820 year: 2015 end-page: 1828 ident: b55 article-title: Characterization methods for the detection of multiple voice disorders: Neurological, functional, and laryngeal diseases publication-title: IEEE J. Biomed. Health Inf. – volume: 10 start-page: 209 year: 2014 end-page: 223 ident: b1 article-title: An efficient voice pathology classification scheme based on applying multi-layer linear discriminant analysis to wavelet packet-based features publication-title: Biomed. Signal Process. Control – volume: 22 start-page: 398 year: 2017 end-page: 408 ident: b58 article-title: Effective glottal instant detection and electroglottographic parameter extraction for automated voice pathology assessment publication-title: IEEE J. Biomed. Health Inf. – volume: 48 start-page: 933 year: 2015 end-page: 940 ident: b83 article-title: Relative entropy collaborative fuzzy clustering method publication-title: Pattern Recognit. – volume: 18 start-page: 1338 year: 2006 end-page: 1351 ident: b85 article-title: Multi-label neural networks with applications to functional genomics and text categorization publication-title: IEEE Trans. Knowl. Data Eng. – volume: 6 start-page: 6961 year: 2018 end-page: 6974 ident: b3 article-title: Voice pathology detection and classification using auto-correlation and entropy features in different frequency regions publication-title: IEEE Access – year: 2018 ident: b18 article-title: Keras: The python deep learning library. ascl – volume: 73 start-page: 220 year: 2017 end-page: 239 ident: b33 article-title: Learning from class-imbalanced data: Review of methods and applications publication-title: Expert Syst. Appl. – volume: 43 start-page: 699 year: 2013 end-page: 704 ident: b64 article-title: Wavelet adaptation for automatic voice disorder sorting publication-title: Comput. Biol. Med. – volume: 7 start-page: 8218 year: 2020 end-page: 8227 ident: b80 article-title: Locational detection of false data injection attack in smart grid: A multi-label classification approach publication-title: IEEE Internet Things J. – volume: 6 start-page: 517 year: 2019 end-page: 520 ident: b79 article-title: Dysfluency recognition by using spectral entropy features publication-title: Int. J. Eng. Adv. Technol. (IJEAT) – volume: 4 start-page: 605 year: 2016 end-page: 627 ident: b11 article-title: Entropy as a measure of mixedupness of realizations in child speech publication-title: Poznan Stud. Contemp. Linguistics – reference: Vikram, C.M., Umarani, K., 2013. Phoneme independent pathological voice detection using wavelet bases, MFCCs and GMM-SVM hybrid classifier. In: International Conference on Advances in Computing, Communications and Informatics. ICACCI, Chengdu, China, pp. 153–156. – volume: 57 start-page: 257 year: 2017 end-page: 265 ident: b7 article-title: An incremental method combining density clustering and support vector machines for voice pathology detection publication-title: Comput. Electr. Eng. – volume: 26 start-page: 817e19 year: 2012 end-page: 27 ident: b54 article-title: Multidirectional regression (MDR)-based features for automatic voice disorder detection publication-title: J. Voice – volume: 151 start-page: 41 year: 2018 end-page: 54 ident: b21 article-title: Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning publication-title: Methods – volume: 154 start-page: 89 year: 2018 end-page: 97 ident: b22 article-title: Diadochokinesis-based expert system considering articulatory features of plosive consonants for early detection of Parkinson’s disease publication-title: Comput. Methods Programs Biomed. – volume: 126 start-page: 125 year: 2016 end-page: 133 ident: b87 article-title: Nonlinear signal processing for vocal folds damage detection based on heterogeneous sensor network publication-title: Signal Process. – start-page: 326 year: 1965 end-page: 334 ident: b19 article-title: Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition publication-title: IEEE Trans. Electron. Comput. – volume: 26 start-page: 817.e19 year: 2012 end-page: 817.e27 ident: b53 article-title: Multidirectional regression (MDR)-based features for automatic voice disorder detection publication-title: J. Voice – reference: Sasou, A., 2017. Automatic identification of pathological voice quality based on the GRBAS categorization. In: Asia-Pacific and Information Processing Association Annual Summit and Conference. APSIPA ASC, Malaysia, pp. 1243–1247. – volume: 19 start-page: 1938 year: 2011 end-page: 1948 ident: b46 article-title: Voice pathology detection and discrimination based on modulation spectral features publication-title: IEEE Trans. Audio, Speech, Lang. Process. – volume: 28 start-page: 44 year: 2008 end-page: 48 ident: b25 article-title: Normal versus pathological voice signals: Using wavelet analysis and support vector machines publication-title: IEEE Eng. Med. Biol. Mag. – volume: 93 start-page: 12 year: 2013 end-page: 22 ident: b71 article-title: Relative entropy rate based model selection for linear hybrid system filters of uncertain nonlinear systems publication-title: Signal Process. – volume: 9 start-page: 634 year: 2013 end-page: 644 ident: b42 article-title: Iloc-animal: A multi-label learning classifier for predicting subcellular localization of animal proteins publication-title: Mol. Biosyst. – volume: 51 start-page: 181 year: 2019 end-page: 199 ident: b29 article-title: On the design of automatic voice condition analysis systems, Part I: Review of concepts and an insight to the state of the art publication-title: Biomed. Signal Process. Control – volume: 47 start-page: 1494 year: 2014 end-page: 1508 ident: b51 article-title: Dependent binary relevance models for multi-label classification publication-title: Pattern Recognit. – volume: 48 start-page: 221 year: 2019 end-page: 233 ident: b8 article-title: Classification of acoustic signals with new feature: Fibonacci space (FSp) publication-title: Biomed. Signal Process. Control – volume: 384 start-page: 205 year: 2017 end-page: 219 ident: b69 article-title: Medical decision support system for extremely imbalanced datasets publication-title: Inform. Sci. – volume: 95 start-page: 88 year: 2019 end-page: 95 ident: b63 article-title: Predicting hospital associated disability from imbalanced data using supervised learning publication-title: Artif. Intell. Med. – volume: 30 start-page: 757.e7 issue: 6 year: 2016 ident: 10.1016/j.specom.2023.102952_b4 article-title: Automatic voice pathology detection with running speech by using estimation of auditory spectrum and cepstral coefficients based on the all-pole model publication-title: J. Voice doi: 10.1016/j.jvoice.2015.08.010 – ident: 10.1016/j.specom.2023.102952_b44 doi: 10.1145/3077136.3080834 – volume: 10 start-page: 24 issue: 2 year: 2018 ident: 10.1016/j.specom.2023.102952_b62 article-title: The utiml package: Multi-label classification in R publication-title: R J. doi: 10.32614/RJ-2018-041 – volume: 11 start-page: 1 year: 2014 ident: 10.1016/j.specom.2023.102952_b52 article-title: Pathological voice detection and binary classification using MPEG-7 audio features publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2014.02.001 – volume: 171 start-page: 9 year: 2019 ident: 10.1016/j.specom.2023.102952_b6 article-title: Implementation of voice pathology detection system using feature selection publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2018.12.019 – volume: 54 start-page: 359 issue: 3 year: 2018 ident: 10.1016/j.specom.2023.102952_b56 article-title: Correlation analysis of performance measures for multi-label classification publication-title: Inf. Process. Manage. doi: 10.1016/j.ipm.2018.01.002 – volume: 18 start-page: 1338 issue: 10 year: 2006 ident: 10.1016/j.specom.2023.102952_b85 article-title: Multi-label neural networks with applications to functional genomics and text categorization publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2006.162 – volume: 65 start-page: 34 year: 2015 ident: 10.1016/j.specom.2023.102952_b88 article-title: Performance comparison of multi-label learning algorithms on clinical data for chronic diseases publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2015.07.017 – volume: 20 start-page: 150 issue: 150 year: 1948 ident: 10.1016/j.specom.2023.102952_b41 article-title: The influence of interaural phase relations upon the masking of speech by white noise publication-title: J. Acoust. Soc. Am. doi: 10.1121/1.1906358 – volume: 73 start-page: 220 year: 2017 ident: 10.1016/j.specom.2023.102952_b33 article-title: Learning from class-imbalanced data: Review of methods and applications publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2016.12.035 – volume: 93 start-page: 12 year: 2013 ident: 10.1016/j.specom.2023.102952_b71 article-title: Relative entropy rate based model selection for linear hybrid system filters of uncertain nonlinear systems publication-title: Signal Process. doi: 10.1016/j.sigpro.2012.05.028 – start-page: 177 issue: 5 year: 2009 ident: 10.1016/j.specom.2023.102952_b23 article-title: A tutorial on multi-label classification techniques publication-title: Found. Comput. Intell. – volume: 42 start-page: 134 year: 2018 ident: 10.1016/j.specom.2023.102952_b77 article-title: A methodology for voice classification based on the personalized fundamental frequency estimation publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2018.01.007 – ident: 10.1016/j.specom.2023.102952_b28 doi: 10.1007/978-3-540-24775-3_5 – volume: 167 start-page: 94 year: 2015 ident: 10.1016/j.specom.2023.102952_b49 article-title: Robust and complex approach of pathological speech signal analysis publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.02.085 – ident: 10.1016/j.specom.2023.102952_b66 doi: 10.1109/APSIPA.2017.8282229 – volume: 154 start-page: 89 year: 2018 ident: 10.1016/j.specom.2023.102952_b22 article-title: Diadochokinesis-based expert system considering articulatory features of plosive consonants for early detection of Parkinson’s disease publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2017.11.010 – volume: 58 start-page: 51 issue: 1 year: 2013 ident: 10.1016/j.specom.2023.102952_b39 article-title: Prediction of body mass index status from voice signals based on machine learning for automated medical applications publication-title: Artif. Intell. Med. doi: 10.1016/j.artmed.2013.02.001 – start-page: 667 year: 2009 ident: 10.1016/j.specom.2023.102952_b73 article-title: Mining multi-label data – volume: 57 start-page: 257 year: 2017 ident: 10.1016/j.specom.2023.102952_b7 article-title: An incremental method combining density clustering and support vector machines for voice pathology detection publication-title: Comput. Electr. Eng. doi: 10.1016/j.compeleceng.2016.08.021 – volume: 28 start-page: 44 issue: 5 year: 2008 ident: 10.1016/j.specom.2023.102952_b25 article-title: Normal versus pathological voice signals: Using wavelet analysis and support vector machines publication-title: IEEE Eng. Med. Biol. Mag. doi: 10.1109/MEMB.2009.934248 – start-page: 460 year: 2016 ident: 10.1016/j.specom.2023.102952_b40 article-title: Deep neural networks for czech multi-label document classification – volume: 6 start-page: 6961 year: 2018 ident: 10.1016/j.specom.2023.102952_b3 article-title: Voice pathology detection and classification using auto-correlation and entropy features in different frequency regions publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2696056 – volume: 3 start-page: 1025 year: 2003 ident: 10.1016/j.specom.2023.102952_b20 article-title: A family of additive online algorithms for category ranking publication-title: J. Mach. Learn. Res. – volume: 10 start-page: 495 issue: 5 year: 2015 ident: 10.1016/j.specom.2023.102952_b14 article-title: Edema and nodule pathological voice identification by SVM classifier on speech signal publication-title: Comput. Softw. – ident: 10.1016/j.specom.2023.102952_b50 doi: 10.1109/ICASSP.2004.1325955 – volume: 95 start-page: 88 year: 2019 ident: 10.1016/j.specom.2023.102952_b63 article-title: Predicting hospital associated disability from imbalanced data using supervised learning publication-title: Artif. Intell. Med. doi: 10.1016/j.artmed.2018.09.004 – start-page: 225 issue: 31 year: 2000 ident: 10.1016/j.specom.2023.102952_b24 article-title: The NIST speaker recognition evaluation: Overview, methodology, systems, results, perspective publication-title: Speech Commun. doi: 10.1016/S0167-6393(99)00080-1 – volume: 13 start-page: 31 issue: 1 year: 2012 ident: 10.1016/j.specom.2023.102952_b82 article-title: Entropy/cross entropy-based group decision making under intuitionistic fuzzy environment publication-title: Inform. Fusion doi: 10.1016/j.inffus.2010.12.001 – volume: 4 start-page: 605 issue: 52 year: 2016 ident: 10.1016/j.specom.2023.102952_b11 article-title: Entropy as a measure of mixedupness of realizations in child speech publication-title: Poznan Stud. Contemp. Linguistics – volume: 19 start-page: 1820 issue: 6 year: 2015 ident: 10.1016/j.specom.2023.102952_b55 article-title: Characterization methods for the detection of multiple voice disorders: Neurological, functional, and laryngeal diseases publication-title: IEEE J. Biomed. Health Inf. doi: 10.1109/JBHI.2015.2467375 – volume: 42 start-page: 1083 issue: 3 year: 2015 ident: 10.1016/j.specom.2023.102952_b43 article-title: A multi-label classification based approach for sentiment classification publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.08.036 – volume: 2 start-page: 69 issue: 2 year: 2007 ident: 10.1016/j.specom.2023.102952_b26 article-title: Novel approach for fetal heart rate classification introducing grammatical evolution publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2007.05.003 – volume: 105 start-page: 248 year: 2016 ident: 10.1016/j.specom.2023.102952_b31 article-title: ZCR-aided neurocomputing: A study with applications publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2016.05.011 – volume: 69 start-page: 270 year: 2016 ident: 10.1016/j.specom.2023.102952_b35 article-title: Voice data mining for laryngeal pathology assessment publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2015.07.026 – volume: 126 start-page: 125 year: 2016 ident: 10.1016/j.specom.2023.102952_b87 article-title: Nonlinear signal processing for vocal folds damage detection based on heterogeneous sensor network publication-title: Signal Process. doi: 10.1016/j.sigpro.2015.08.019 – start-page: 201 issue: 9 year: 2001 ident: 10.1016/j.specom.2023.102952_b86 article-title: Non-linear feature based classification of speech under stress publication-title: IEEE Trans. Speech Audio Process. doi: 10.1109/89.905995 – volume: 384 start-page: 205 year: 2017 ident: 10.1016/j.specom.2023.102952_b69 article-title: Medical decision support system for extremely imbalanced datasets publication-title: Inform. Sci. doi: 10.1016/j.ins.2016.08.077 – volume: 56 start-page: 1 year: 2016 ident: 10.1016/j.specom.2023.102952_b84 article-title: Feature selection in mixed data: A method using a novel fuzzy rough set-based information entropy publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2016.02.013 – volume: 151 start-page: 41 year: 2018 ident: 10.1016/j.specom.2023.102952_b21 article-title: Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning publication-title: Methods doi: 10.1016/j.ymeth.2018.07.007 – start-page: 406 year: 2007 ident: 10.1016/j.specom.2023.102952_b74 article-title: Random k-labelsets: An ensemble method for multilabel classification – volume: 54 start-page: 720 issue: 5 year: 1966 ident: 10.1016/j.specom.2023.102952_b67 article-title: Vocoders: Analysis and synthesis of speech publication-title: Proc. IEEE doi: 10.1109/PROC.1966.4841 – volume: 6 start-page: 16246 year: 2018 ident: 10.1016/j.specom.2023.102952_b76 article-title: Voice disorder identification by using machine learning techniques publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2816338 – volume: 41 start-page: 161 year: 2018 ident: 10.1016/j.specom.2023.102952_b32 article-title: A tutorial-review on entropy-based handcrafted feature extraction for information fusion publication-title: Inf. Fusion doi: 10.1016/j.inffus.2017.09.006 – ident: 10.1016/j.specom.2023.102952_b38 doi: 10.1109/CIST.2014.7016648 – volume: 22 start-page: 398 issue: 2 year: 2017 ident: 10.1016/j.specom.2023.102952_b58 article-title: Effective glottal instant detection and electroglottographic parameter extraction for automated voice pathology assessment publication-title: IEEE J. Biomed. Health Inf. – ident: 10.1016/j.specom.2023.102952_b65 doi: 10.1109/KBEI.2015.7436106 – volume: 19 start-page: 1938 issue: 7 year: 2011 ident: 10.1016/j.specom.2023.102952_b46 article-title: Voice pathology detection and discrimination based on modulation spectral features publication-title: IEEE Trans. Audio, Speech, Lang. Process. doi: 10.1109/TASL.2010.2104141 – volume: 68 start-page: 135 issue: 2 year: 2002 ident: 10.1016/j.specom.2023.102952_b45 article-title: Software corrections of vocal disorders publication-title: Comput. Methods Programs Biomed. doi: 10.1016/S0169-2607(01)00161-4 – volume: 26 start-page: 817.e19 issue: 6 year: 2012 ident: 10.1016/j.specom.2023.102952_b53 article-title: Multidirectional regression (MDR)-based features for automatic voice disorder detection publication-title: J. Voice doi: 10.1016/j.jvoice.2012.05.002 – start-page: 326 issue: 3 year: 1965 ident: 10.1016/j.specom.2023.102952_b19 article-title: Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition publication-title: IEEE Trans. Electron. Comput. doi: 10.1109/PGEC.1965.264137 – volume: 43 start-page: 699 issue: 6 year: 2013 ident: 10.1016/j.specom.2023.102952_b64 article-title: Wavelet adaptation for automatic voice disorder sorting publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2013.03.006 – volume: 179 start-page: 264 year: 2016 ident: 10.1016/j.specom.2023.102952_b30 article-title: A tutorial on signal energy and its applications publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.12.012 – year: 2007 ident: 10.1016/j.specom.2023.102952_b12 – start-page: 25 year: 2010 ident: 10.1016/j.specom.2023.102952_b70 – volume: 51 start-page: 181 year: 2019 ident: 10.1016/j.specom.2023.102952_b29 article-title: On the design of automatic voice condition analysis systems, Part I: Review of concepts and an insight to the state of the art publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2018.12.024 – ident: 10.1016/j.specom.2023.102952_b68 – year: 1995 ident: 10.1016/j.specom.2023.102952_b75 – start-page: 99 year: 2012 ident: 10.1016/j.specom.2023.102952_b47 article-title: Voice pathology detection on the Saarbrücken voice database with calibration and fusion of scores using multifocal toolkit – ident: 10.1016/j.specom.2023.102952_b78 doi: 10.1109/ICACC.2013.37 – volume: 320 start-page: 35 year: 2018 ident: 10.1016/j.specom.2023.102952_b5 article-title: Applying multi-label techniques in emotion identification of short texts publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.08.053 – volume: 33 start-page: 947.e11 issue: 6 year: 2019 ident: 10.1016/j.specom.2023.102952_b34 article-title: A survey on machine learning approaches for automatic detection of voice disorders publication-title: J. Voice doi: 10.1016/j.jvoice.2018.07.014 – volume: 10 start-page: 209 year: 2014 ident: 10.1016/j.specom.2023.102952_b1 article-title: An efficient voice pathology classification scheme based on applying multi-layer linear discriminant analysis to wavelet packet-based features publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2013.11.002 – volume: 6 start-page: 517 issue: 8 year: 2019 ident: 10.1016/j.specom.2023.102952_b79 article-title: Dysfluency recognition by using spectral entropy features publication-title: Int. J. Eng. Adv. Technol. (IJEAT) – volume: 6 start-page: 6969 year: 2017 ident: 10.1016/j.specom.2023.102952_b2 article-title: Voice pathology detection and classification using auto-correlation and entropy features in different frequency regions publication-title: IEEE Access – volume: 65 start-page: 219 issue: 3 year: 2015 ident: 10.1016/j.specom.2023.102952_b37 article-title: A hybrid cost-sensitive ensemble for imbalanced breast thermogram classification publication-title: Artif. Intell. Med. doi: 10.1016/j.artmed.2015.07.005 – volume: 138 start-page: 3061 issue: 5 year: 2015 ident: 10.1016/j.specom.2023.102952_b60 article-title: Neural-scaled entropy predicts the effects of nonlinear frequency compression on speech perception publication-title: J. Acoust. Soc. Am. doi: 10.1121/1.4934731 – volume: 9 start-page: 634 issue: 4 year: 2013 ident: 10.1016/j.specom.2023.102952_b42 article-title: Iloc-animal: A multi-label learning classifier for predicting subcellular localization of animal proteins publication-title: Mol. Biosyst. doi: 10.1039/c3mb25466f – volume: 3 start-page: 1 issue: 3 year: 2007 ident: 10.1016/j.specom.2023.102952_b72 article-title: Multi-label classification: An overview publication-title: Int. J. Data Warehous. Min. (IJDWM) doi: 10.4018/jdwm.2007070101 – volume: 168 start-page: 39 year: 2019 ident: 10.1016/j.specom.2023.102952_b10 article-title: A systematic literature review and classification of knowledge discovery in traditional medicine publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2018.10.017 – volume: 177 start-page: 277 year: 2019 ident: 10.1016/j.specom.2023.102952_b27 article-title: Diagnosis of autism spectrum disorder based on complex network features publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2019.06.006 – year: 2011 ident: 10.1016/j.specom.2023.102952_b16 – volume: 48 start-page: 221 year: 2019 ident: 10.1016/j.specom.2023.102952_b8 article-title: Classification of acoustic signals with new feature: Fibonacci space (FSp) publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2018.08.037 – ident: 10.1016/j.specom.2023.102952_b9 doi: 10.1007/978-3-030-00350-0_13 – volume: 16 start-page: 321 year: 2002 ident: 10.1016/j.specom.2023.102952_b17 article-title: Smote: Synthetic minority over-sampling technique publication-title: J. Artificial Intelligence Res. doi: 10.1613/jair.953 – volume: 100 start-page: 279 year: 2018 ident: 10.1016/j.specom.2023.102952_b81 article-title: Multi-label classification methods for improving comorbidities identification publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2017.07.006 – volume: 7 start-page: 8218 issue: 9 year: 2020 ident: 10.1016/j.specom.2023.102952_b80 article-title: Locational detection of false data injection attack in smart grid: A multi-label classification approach publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2020.2983911 – volume: 85 start-page: 333 issue: 3 year: 2011 ident: 10.1016/j.specom.2023.102952_b61 article-title: Classifier chains for multi-label classification publication-title: Mach. Learn. doi: 10.1007/s10994-011-5256-5 – volume: 48 start-page: 933 issue: 3 year: 2015 ident: 10.1016/j.specom.2023.102952_b83 article-title: Relative entropy collaborative fuzzy clustering method publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2014.09.018 – volume: 45 start-page: 5 issue: 1 year: 2001 ident: 10.1016/j.specom.2023.102952_b15 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 9 start-page: 1 issue: 31 year: 2016 ident: 10.1016/j.specom.2023.102952_b57 article-title: An improved prediction of kidney disease using smote publication-title: Indian J. Sci. Technol. – volume: 24 start-page: 15327 year: 2020 ident: 10.1016/j.specom.2023.102952_b36 article-title: Multi-label learning for crop leaf diseases recognition and severity estimation based on convolutional neural networks publication-title: Soft Comput. doi: 10.1007/s00500-020-04866-z – volume: 47 start-page: 1494 issue: 3 year: 2014 ident: 10.1016/j.specom.2023.102952_b51 article-title: Dependent binary relevance models for multi-label classification publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2013.09.029 – volume: 6 start-page: 20 issue: 1 year: 2004 ident: 10.1016/j.specom.2023.102952_b13 article-title: A study of the behavior of several methods for balancing machine learning training data publication-title: ACM SIGKDD Explor. Newsl. doi: 10.1145/1007730.1007735 – year: 2018 ident: 10.1016/j.specom.2023.102952_b18 – volume: 91 start-page: 191 issue: 2 year: 2019 ident: 10.1016/j.specom.2023.102952_b48 article-title: Multi-output tree chaining: An interpretative modelling and lightweight multi-target approach publication-title: J. Signal Process. Syst. doi: 10.1007/s11265-018-1376-5 – year: 2008 ident: 10.1016/j.specom.2023.102952_b59 – volume: 26 start-page: 817e19 issue: 6 year: 2012 ident: 10.1016/j.specom.2023.102952_b54 article-title: Multidirectional regression (MDR)-based features for automatic voice disorder detection publication-title: J. Voice doi: 10.1016/j.jvoice.2012.05.002 |
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