Deep Neural Network for Automatic Classification of Pathological Voice Signals
Computer-aided pathological voice detection is efficient for initial screening of pathological voice, and has received high academic and clinical attention. This paper proposes an automatic diagnosis method of pathological voice based on deep neural network (DNN). Other two classification models (su...
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| Veröffentlicht in: | Journal of voice Jg. 36; H. 2; S. 288.e15 - 288.e24 |
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01.03.2022
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| ISSN: | 0892-1997, 1873-4588, 1873-4588 |
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| Abstract | Computer-aided pathological voice detection is efficient for initial screening of pathological voice, and has received high academic and clinical attention. This paper proposes an automatic diagnosis method of pathological voice based on deep neural network (DNN). Other two classification models (support vector machines and random forests) were used to verify the effectiveness of DNN.
In this paper, we extracted 12 Mel frequency cepstral coefficients of each voice sample as row features. The constructed DNN consists a two-layer stacked sparse autoencoders network and a softmax layer. The stacked sparse autoencoders layer can learn high-level features from raw Mel frequency cepstral coefficients features. Then, the softmax layer can diagnose pathological voice according to high-level features. The DNN and the other two comparison models used the same train set and test set for the experiment.
Experimental results reveal that the value of sensitivity, specificity, precision, accuracy, and F1 score of the DNN can reach 97.8%, 99.4%, 99.4%, 98.6%, and 98.4%, respectively. The five indexes of DNN classification results are at least 6.2%, 5%, 5.6%, 5.7%, and 6.2% higher than the comparison models (support vector machine and random forest).
The proposed DNN can learn advanced features from raw acoustic features, and distinguish pathological voice from healthy voice. To the extent of this preliminary study, future studies can further explore the application of DNN in other experiments and clinical practice |
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| AbstractList | Computer-aided pathological voice detection is efficient for initial screening of pathological voice, and has received high academic and clinical attention. This paper proposes an automatic diagnosis method of pathological voice based on deep neural network (DNN). Other two classification models (support vector machines and random forests) were used to verify the effectiveness of DNN.OBJECTIVESComputer-aided pathological voice detection is efficient for initial screening of pathological voice, and has received high academic and clinical attention. This paper proposes an automatic diagnosis method of pathological voice based on deep neural network (DNN). Other two classification models (support vector machines and random forests) were used to verify the effectiveness of DNN.In this paper, we extracted 12 Mel frequency cepstral coefficients of each voice sample as row features. The constructed DNN consists a two-layer stacked sparse autoencoders network and a softmax layer. The stacked sparse autoencoders layer can learn high-level features from raw Mel frequency cepstral coefficients features. Then, the softmax layer can diagnose pathological voice according to high-level features. The DNN and the other two comparison models used the same train set and test set for the experiment.METHODSIn this paper, we extracted 12 Mel frequency cepstral coefficients of each voice sample as row features. The constructed DNN consists a two-layer stacked sparse autoencoders network and a softmax layer. The stacked sparse autoencoders layer can learn high-level features from raw Mel frequency cepstral coefficients features. Then, the softmax layer can diagnose pathological voice according to high-level features. The DNN and the other two comparison models used the same train set and test set for the experiment.Experimental results reveal that the value of sensitivity, specificity, precision, accuracy, and F1 score of the DNN can reach 97.8%, 99.4%, 99.4%, 98.6%, and 98.4%, respectively. The five indexes of DNN classification results are at least 6.2%, 5%, 5.6%, 5.7%, and 6.2% higher than the comparison models (support vector machine and random forest).RESULTSExperimental results reveal that the value of sensitivity, specificity, precision, accuracy, and F1 score of the DNN can reach 97.8%, 99.4%, 99.4%, 98.6%, and 98.4%, respectively. The five indexes of DNN classification results are at least 6.2%, 5%, 5.6%, 5.7%, and 6.2% higher than the comparison models (support vector machine and random forest).The proposed DNN can learn advanced features from raw acoustic features, and distinguish pathological voice from healthy voice. To the extent of this preliminary study, future studies can further explore the application of DNN in other experiments and clinical practice.CONCLUSIONSThe proposed DNN can learn advanced features from raw acoustic features, and distinguish pathological voice from healthy voice. To the extent of this preliminary study, future studies can further explore the application of DNN in other experiments and clinical practice. Computer-aided pathological voice detection is efficient for initial screening of pathological voice, and has received high academic and clinical attention. This paper proposes an automatic diagnosis method of pathological voice based on deep neural network (DNN). Other two classification models (support vector machines and random forests) were used to verify the effectiveness of DNN. In this paper, we extracted 12 Mel frequency cepstral coefficients of each voice sample as row features. The constructed DNN consists a two-layer stacked sparse autoencoders network and a softmax layer. The stacked sparse autoencoders layer can learn high-level features from raw Mel frequency cepstral coefficients features. Then, the softmax layer can diagnose pathological voice according to high-level features. The DNN and the other two comparison models used the same train set and test set for the experiment. Experimental results reveal that the value of sensitivity, specificity, precision, accuracy, and F1 score of the DNN can reach 97.8%, 99.4%, 99.4%, 98.6%, and 98.4%, respectively. The five indexes of DNN classification results are at least 6.2%, 5%, 5.6%, 5.7%, and 6.2% higher than the comparison models (support vector machine and random forest). The proposed DNN can learn advanced features from raw acoustic features, and distinguish pathological voice from healthy voice. To the extent of this preliminary study, future studies can further explore the application of DNN in other experiments and clinical practice. SummaryObjectivesComputer-aided pathological voice detection is efficient for initial screening of pathological voice, and has received high academic and clinical attention. This paper proposes an automatic diagnosis method of pathological voice based on deep neural network (DNN). Other two classification models (support vector machines and random forests) were used to verify the effectiveness of DNN. MethodsIn this paper, we extracted 12 Mel frequency cepstral coefficients of each voice sample as row features. The constructed DNN consists a two-layer stacked sparse autoencoders network and a softmax layer. The stacked sparse autoencoders layer can learn high-level features from raw Mel frequency cepstral coefficients features. Then, the softmax layer can diagnose pathological voice according to high-level features. The DNN and the other two comparison models used the same train set and test set for the experiment. ResultsExperimental results reveal that the value of sensitivity, specificity, precision, accuracy, and F1 score of the DNN can reach 97.8%, 99.4%, 99.4%, 98.6%, and 98.4%, respectively. The five indexes of DNN classification results are at least 6.2%, 5%, 5.6%, 5.7%, and 6.2% higher than the comparison models (support vector machine and random forest). ConclusionsThe proposed DNN can learn advanced features from raw acoustic features, and distinguish pathological voice from healthy voice. To the extent of this preliminary study, future studies can further explore the application of DNN in other experiments and clinical practice Computer-aided pathological voice detection is efficient for initial screening of pathological voice, and has received high academic and clinical attention. This paper proposes an automatic diagnosis method of pathological voice based on deep neural network (DNN). Other two classification models (support vector machines and random forests) were used to verify the effectiveness of DNN. In this paper, we extracted 12 Mel frequency cepstral coefficients of each voice sample as row features. The constructed DNN consists a two-layer stacked sparse autoencoders network and a softmax layer. The stacked sparse autoencoders layer can learn high-level features from raw Mel frequency cepstral coefficients features. Then, the softmax layer can diagnose pathological voice according to high-level features. The DNN and the other two comparison models used the same train set and test set for the experiment. Experimental results reveal that the value of sensitivity, specificity, precision, accuracy, and F1 score of the DNN can reach 97.8%, 99.4%, 99.4%, 98.6%, and 98.4%, respectively. The five indexes of DNN classification results are at least 6.2%, 5%, 5.6%, 5.7%, and 6.2% higher than the comparison models (support vector machine and random forest). The proposed DNN can learn advanced features from raw acoustic features, and distinguish pathological voice from healthy voice. To the extent of this preliminary study, future studies can further explore the application of DNN in other experiments and clinical practice |
| Author | Chen, Junjiang Chen, Lili |
| Author_xml | – sequence: 1 givenname: Lili surname: Chen fullname: Chen, Lili email: clili522@cqjtu.edu.cn organization: School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing, China – sequence: 2 givenname: Junjiang surname: Chen fullname: Chen, Junjiang organization: School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32660846$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.neucom.2018.05.040 10.1118/1.4896099 10.1016/j.neunet.2017.04.012 10.1016/j.neucom.2016.12.038 10.1016/j.neunet.2014.08.005 10.1016/j.bspc.2019.01.007 10.1016/j.compeleceng.2018.04.008 10.1016/j.compbiomed.2011.06.019 10.1016/j.jmr.2018.06.015 10.1016/j.jvoice.2009.08.002 10.1016/j.procs.2018.10.010 10.1016/j.jvoice.2010.04.009 10.1016/j.compbiomed.2018.05.027 10.1016/j.procs.2017.12.112 10.1016/j.compbiomed.2009.10.011 10.1016/j.jvoice.2014.05.006 10.1016/j.eswa.2018.08.050 10.1016/j.jvoice.2018.07.014 10.1109/TNSRE.2018.2805338 10.1016/j.neucom.2017.08.043 10.1016/j.patrec.2005.07.004 10.17743/jaes.2019.0004 10.1016/j.asoc.2014.03.036 10.1016/j.measurement.2016.04.007 10.1126/science.1127647 10.1016/j.ins.2016.01.082 10.1007/s11517-019-01949-4 10.1016/j.bspc.2014.02.001 10.1016/j.nicl.2018.01.032 10.1016/j.ins.2017.10.044 |
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| Keywords | Deep neural network Automatic classification Pathological voice Stacked sparse autoencoder Mel frequency cepstral coefficients |
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| References | Al-Noori, Duncan (bib0025) 2019; 67 Wang, Zhang, Yan (bib0016) 2011; 25 AL, LAN, L (bib0024) 2000; 101 Hegde, Shetty, Rai (bib0003) 2018; 33 Liu, Wang, Liu (bib0010) 2017; 234 Vaziri, Almasganj, Behroozmand (bib0015) 2010; 40 Chen, Zhou, Su (bib0021) 2018; 428 Lee, Chou, Han (bib0026) 2006; 27 Adem, Kilicarslan, Comert (bib0022) 2019; 115 Sanchez, Sidky, Pan (bib0009) 2014; 41 Sun, Shao, Zhao (bib0033) 2016; 89 Zeng, Zhang, Song (bib0031) 2018; 273 Muhammad, Melhem (bib0001) 2014; 11 Aichinger, Pernkopf, Schoentgen (bib0004) 2019; 50 Sainath, Kingsbury, Saon (bib0011) 2015; 64 Zorrilla, Zapirain, Izquierdo (bib0012) 2012; 1 Uloza, Verikas, Bacauskiene (bib0005) 2011; 25 Erfanian Saeedi, Almasganj, Torabinejad (bib0014) 2011; 41 Jothilakshmi (bib0007) 2014; 21 Delvaux, Pillot-Loiseau (bib0002) 2019 Hinton, Salakhutdinov (bib0018) 2006; 313 Maurya, Kumar, Agarwal (bib0017) 2018; 125 Cesari, Pietro, Marciano (bib0023) 2018; 68 Cordeiro, Ribeiro (bib0013) 2018; 138 Xu, Cao, Song (bib0030) 2018; 311 Praveen, Agrawal, Sundaram (bib0019) 2018; 99 He, Li, Holland (bib0029) 2018; 18 Wang, Qian, Feng (bib0034) 2018; 26 Yamauchi, Yokonishi, Imagawa (bib0006) 2015; 29 Hu, Yu (bib0028) 2019; 17 Al Rahhal, Bazi, AlHichri (bib0020) 2016; 345 Naeem, Hamzaid, Islam (bib0027) 2019; 57 Ayinde, Zurada (bib0032) 2017; 93 Qiao, Zhang, Pan (bib0008) 2018; 294 Aichinger (10.1016/j.jvoice.2020.05.029_bib0004) 2019; 50 Jothilakshmi (10.1016/j.jvoice.2020.05.029_bib0007) 2014; 21 Wang (10.1016/j.jvoice.2020.05.029_bib0016) 2011; 25 Wang (10.1016/j.jvoice.2020.05.029_bib0034) 2018; 26 Sanchez (10.1016/j.jvoice.2020.05.029_bib0009) 2014; 41 Ayinde (10.1016/j.jvoice.2020.05.029_bib0032) 2017; 93 Naeem (10.1016/j.jvoice.2020.05.029_bib0027) 2019; 57 Al Rahhal (10.1016/j.jvoice.2020.05.029_bib0020) 2016; 345 Maurya (10.1016/j.jvoice.2020.05.029_bib0017) 2018; 125 Al-Noori (10.1016/j.jvoice.2020.05.029_bib0025) 2019; 67 Muhammad (10.1016/j.jvoice.2020.05.029_bib0001) 2014; 11 Chen (10.1016/j.jvoice.2020.05.029_bib0021) 2018; 428 Zeng (10.1016/j.jvoice.2020.05.029_bib0031) 2018; 273 Xu (10.1016/j.jvoice.2020.05.029_bib0030) 2018; 311 Hinton (10.1016/j.jvoice.2020.05.029_bib0018) 2006; 313 Zorrilla (10.1016/j.jvoice.2020.05.029_bib0012) 2012; 1 Erfanian Saeedi (10.1016/j.jvoice.2020.05.029_bib0014) 2011; 41 Sainath (10.1016/j.jvoice.2020.05.029_bib0011) 2015; 64 AL (10.1016/j.jvoice.2020.05.029_bib0024) 2000; 101 Lee (10.1016/j.jvoice.2020.05.029_bib0026) 2006; 27 Hu (10.1016/j.jvoice.2020.05.029_bib0028) 2019; 17 Delvaux (10.1016/j.jvoice.2020.05.029_bib0002) 2019 Yamauchi (10.1016/j.jvoice.2020.05.029_bib0006) 2015; 29 Qiao (10.1016/j.jvoice.2020.05.029_bib0008) 2018; 294 Liu (10.1016/j.jvoice.2020.05.029_bib0010) 2017; 234 Vaziri (10.1016/j.jvoice.2020.05.029_bib0015) 2010; 40 Uloza (10.1016/j.jvoice.2020.05.029_bib0005) 2011; 25 Cesari (10.1016/j.jvoice.2020.05.029_bib0023) 2018; 68 He (10.1016/j.jvoice.2020.05.029_bib0029) 2018; 18 Sun (10.1016/j.jvoice.2020.05.029_bib0033) 2016; 89 Adem (10.1016/j.jvoice.2020.05.029_bib0022) 2019; 115 Cordeiro (10.1016/j.jvoice.2020.05.029_bib0013) 2018; 138 Praveen (10.1016/j.jvoice.2020.05.029_bib0019) 2018; 99 Hegde (10.1016/j.jvoice.2020.05.029_bib0003) 2018; 33 |
| References_xml | – volume: 26 start-page: 629 year: 2018 end-page: 636 ident: bib0034 article-title: Design and preliminary evaluation of electrolarynx with F0 control based on capacitive touch technology publication-title: IEEE Trans Neural Syst Rehabil Eng – volume: 138 start-page: 64 year: 2018 end-page: 71 ident: bib0013 article-title: Spectral envelope first peak and periodic component in pathological voices: a spectral analysis publication-title: Proc Comput Sci – volume: 40 start-page: 54 year: 2010 end-page: 63 ident: bib0015 article-title: Pathological assessment of patients’ speech signals using nonlinear dynamical analysis publication-title: Comput Biol Med – volume: 99 start-page: 38 year: 2018 end-page: 52 ident: bib0019 article-title: Ischemic stroke lesion segmentation using stacked sparse autoencoder publication-title: Comput Biol Med – volume: 1 year: 2012 ident: bib0012 publication-title: Computer Aided Tool for Diagnosis of ENT Pathologies Using Digital Signal Processing of Speech and Stroboscopic Images – volume: 25 start-page: 38 year: 2011 end-page: 43 ident: bib0016 article-title: Discrimination between pathological and normal voices using GMM-SVM approach publication-title: J Voice – volume: 115 start-page: 557 year: 2019 end-page: 564 ident: bib0022 article-title: Classification and diagnosis of cervical cancer with softmax classification with stacked autoencoder publication-title: Expert Syst Appl – volume: 41 year: 2014 ident: bib0009 article-title: Task-based optimization of dedicated breast CT via Hotelling observer metrics publication-title: Med Phys – volume: 17 start-page: 1483 year: 2019 end-page: 1490 ident: bib0028 article-title: Diagnosis of mesothelioma with deep learning publication-title: Oncol Lett – volume: 89 start-page: 171 year: 2016 end-page: 178 ident: bib0033 article-title: A sparse auto-encoder-based deep neural network approach for induction motor faults classification publication-title: Measurement – volume: 29 start-page: 109 year: 2015 end-page: 119 ident: bib0006 article-title: Quantitative analysis of digital videokymography: a preliminary study on age- and gender-related difference of vocal fold vibration in normal speakers publication-title: J Voice – volume: 27 start-page: 93 year: 2006 end-page: 101 ident: bib0026 article-title: Automatic recognition of animal vocalizations using averaged MFCC and linear discriminant analysis publication-title: Pattern Recognit Lett – volume: 311 start-page: 1 year: 2018 end-page: 10 ident: bib0030 article-title: ircuit fault diagnosis of power rectifier using sparse autoencoder based deep neural network publication-title: Neurocomputing – volume: 25 start-page: 700 year: 2011 end-page: 708 ident: bib0005 article-title: Categorizing normal and pathological voices: automated and perceptual categorization publication-title: J Voice – volume: 428 start-page: 49 year: 2018 end-page: 61 ident: bib0021 article-title: Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction publication-title: Inf Sci – volume: 125 start-page: 880 year: 2018 end-page: 887 ident: bib0017 article-title: Speaker recognition for Hindi speech signal using MFCC-GMM approach publication-title: Proc Comput Sci – volume: 33 start-page: 947.e11 year: 2018 end-page: 947.e33 ident: bib0003 article-title: A survey on machine learning approaches for automatic detection of voice disorders publication-title: J Voice – volume: 234 start-page: 11 year: 2017 end-page: 26 ident: bib0010 article-title: A survey of deep neural network architectures and their applications publication-title: Neurocomputing – year: 2019 ident: bib0002 article-title: Perceptual judgment of voice quality in nondysphonic French speakers: effect of task-, speaker- and listener-related variables publication-title: J Voice – volume: 93 start-page: 99 year: 2017 end-page: 109 ident: bib0032 article-title: Nonredundant sparse feature extraction using autoencoders with receptive fields clustering publication-title: Neural Netw – volume: 21 start-page: 244 year: 2014 end-page: 249 ident: bib0007 article-title: Automatic system to detect the type of voice pathology publication-title: Appl Soft Comput – volume: 64 start-page: 39 year: 2015 end-page: 48 ident: bib0011 article-title: Deep convolutional neural networks for large-scale speech tasks publication-title: Neural Netw – volume: 57 start-page: 1199 year: 2019 end-page: 1211 ident: bib0027 article-title: Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury publication-title: Med Biol Eng Comput – volume: 41 start-page: 822 year: 2011 end-page: 828 ident: bib0014 article-title: Support vector wavelet adaptation for pathological voice assessment publication-title: Comput Biol Med – volume: 18 start-page: 290 year: 2018 end-page: 297 ident: bib0029 article-title: Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework publication-title: Neuroimage Clin – volume: 345 start-page: 340 year: 2016 end-page: 354 ident: bib0020 article-title: Deep learning approach for active classification of electrocardiogram signals publication-title: Inf Sci – volume: 11 start-page: 1 year: 2014 end-page: 9 ident: bib0001 article-title: Pathological voice detection and binary classification using MPEG-7 audio features publication-title: Biomed Signal Process Control – volume: 50 start-page: 158 year: 2019 end-page: 167 ident: bib0004 article-title: Detection of extra pulses in synthesized glottal area waveforms of dysphonic voices publication-title: Biomed Signal Process Control – volume: 67 start-page: 174 year: 2019 end-page: 189 ident: bib0025 article-title: Robust speaker recognition in noisy conditions by means of online training with noise profiles publication-title: J Audio Eng Soc – volume: 273 start-page: 643 year: 2018 end-page: 649 ident: bib0031 article-title: Facial expression recognition via learning deep sparse autoencoders publication-title: Neurocomputing – volume: 313 start-page: 504 year: 2006 end-page: 507 ident: bib0018 article-title: Reducing the dimensionality of data with neural networks publication-title: Science – volume: 294 start-page: 24 year: 2018 end-page: 34 ident: bib0008 article-title: Optimization-based image reconstruction from sparsely sampled data in electron paramagnetic resonance imaging publication-title: J Magn Reson – volume: 68 start-page: 310 year: 2018 end-page: 321 ident: bib0023 article-title: A new database of healthy and pathological voices publication-title: Comput Electr Eng – volume: 101 start-page: e215 year: 2000 end-page: e220 ident: bib0024 article-title: PhysioBank, PhysioToolkit, and PhysioNet. Components of a new research resource for complex physiologic signals publication-title: Circulation – volume: 311 start-page: 1 year: 2018 ident: 10.1016/j.jvoice.2020.05.029_bib0030 article-title: ircuit fault diagnosis of power rectifier using sparse autoencoder based deep neural network publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.05.040 – volume: 41 year: 2014 ident: 10.1016/j.jvoice.2020.05.029_bib0009 article-title: Task-based optimization of dedicated breast CT via Hotelling observer metrics publication-title: Med Phys doi: 10.1118/1.4896099 – volume: 17 start-page: 1483 year: 2019 ident: 10.1016/j.jvoice.2020.05.029_bib0028 article-title: Diagnosis of mesothelioma with deep learning publication-title: Oncol Lett – volume: 93 start-page: 99 year: 2017 ident: 10.1016/j.jvoice.2020.05.029_bib0032 article-title: Nonredundant sparse feature extraction using autoencoders with receptive fields clustering publication-title: Neural Netw doi: 10.1016/j.neunet.2017.04.012 – volume: 234 start-page: 11 year: 2017 ident: 10.1016/j.jvoice.2020.05.029_bib0010 article-title: A survey of deep neural network architectures and their applications publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.12.038 – volume: 101 start-page: e215 year: 2000 ident: 10.1016/j.jvoice.2020.05.029_bib0024 article-title: PhysioBank, PhysioToolkit, and PhysioNet. Components of a new research resource for complex physiologic signals publication-title: Circulation – volume: 64 start-page: 39 year: 2015 ident: 10.1016/j.jvoice.2020.05.029_bib0011 article-title: Deep convolutional neural networks for large-scale speech tasks publication-title: Neural Netw doi: 10.1016/j.neunet.2014.08.005 – volume: 50 start-page: 158 year: 2019 ident: 10.1016/j.jvoice.2020.05.029_bib0004 article-title: Detection of extra pulses in synthesized glottal area waveforms of dysphonic voices publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2019.01.007 – volume: 68 start-page: 310 year: 2018 ident: 10.1016/j.jvoice.2020.05.029_bib0023 article-title: A new database of healthy and pathological voices publication-title: Comput Electr Eng doi: 10.1016/j.compeleceng.2018.04.008 – volume: 41 start-page: 822 year: 2011 ident: 10.1016/j.jvoice.2020.05.029_bib0014 article-title: Support vector wavelet adaptation for pathological voice assessment publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2011.06.019 – volume: 294 start-page: 24 year: 2018 ident: 10.1016/j.jvoice.2020.05.029_bib0008 article-title: Optimization-based image reconstruction from sparsely sampled data in electron paramagnetic resonance imaging publication-title: J Magn Reson doi: 10.1016/j.jmr.2018.06.015 – volume: 25 start-page: 38 year: 2011 ident: 10.1016/j.jvoice.2020.05.029_bib0016 article-title: Discrimination between pathological and normal voices using GMM-SVM approach publication-title: J Voice doi: 10.1016/j.jvoice.2009.08.002 – volume: 1 year: 2012 ident: 10.1016/j.jvoice.2020.05.029_bib0012 – volume: 138 start-page: 64 year: 2018 ident: 10.1016/j.jvoice.2020.05.029_bib0013 article-title: Spectral envelope first peak and periodic component in pathological voices: a spectral analysis publication-title: Proc Comput Sci doi: 10.1016/j.procs.2018.10.010 – volume: 25 start-page: 700 year: 2011 ident: 10.1016/j.jvoice.2020.05.029_bib0005 article-title: Categorizing normal and pathological voices: automated and perceptual categorization publication-title: J Voice doi: 10.1016/j.jvoice.2010.04.009 – volume: 99 start-page: 38 year: 2018 ident: 10.1016/j.jvoice.2020.05.029_bib0019 article-title: Ischemic stroke lesion segmentation using stacked sparse autoencoder publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2018.05.027 – volume: 125 start-page: 880 year: 2018 ident: 10.1016/j.jvoice.2020.05.029_bib0017 article-title: Speaker recognition for Hindi speech signal using MFCC-GMM approach publication-title: Proc Comput Sci doi: 10.1016/j.procs.2017.12.112 – volume: 40 start-page: 54 year: 2010 ident: 10.1016/j.jvoice.2020.05.029_bib0015 article-title: Pathological assessment of patients’ speech signals using nonlinear dynamical analysis publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2009.10.011 – volume: 29 start-page: 109 year: 2015 ident: 10.1016/j.jvoice.2020.05.029_bib0006 article-title: Quantitative analysis of digital videokymography: a preliminary study on age- and gender-related difference of vocal fold vibration in normal speakers publication-title: J Voice doi: 10.1016/j.jvoice.2014.05.006 – volume: 115 start-page: 557 year: 2019 ident: 10.1016/j.jvoice.2020.05.029_bib0022 article-title: Classification and diagnosis of cervical cancer with softmax classification with stacked autoencoder publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2018.08.050 – year: 2019 ident: 10.1016/j.jvoice.2020.05.029_bib0002 article-title: Perceptual judgment of voice quality in nondysphonic French speakers: effect of task-, speaker- and listener-related variables publication-title: J Voice – volume: 33 start-page: 947.e11 year: 2018 ident: 10.1016/j.jvoice.2020.05.029_bib0003 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: 26 start-page: 629 year: 2018 ident: 10.1016/j.jvoice.2020.05.029_bib0034 article-title: Design and preliminary evaluation of electrolarynx with F0 control based on capacitive touch technology publication-title: IEEE Trans Neural Syst Rehabil Eng doi: 10.1109/TNSRE.2018.2805338 – volume: 273 start-page: 643 year: 2018 ident: 10.1016/j.jvoice.2020.05.029_bib0031 article-title: Facial expression recognition via learning deep sparse autoencoders publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.08.043 – volume: 27 start-page: 93 year: 2006 ident: 10.1016/j.jvoice.2020.05.029_bib0026 article-title: Automatic recognition of animal vocalizations using averaged MFCC and linear discriminant analysis publication-title: Pattern Recognit Lett doi: 10.1016/j.patrec.2005.07.004 – volume: 67 start-page: 174 year: 2019 ident: 10.1016/j.jvoice.2020.05.029_bib0025 article-title: Robust speaker recognition in noisy conditions by means of online training with noise profiles publication-title: J Audio Eng Soc doi: 10.17743/jaes.2019.0004 – volume: 21 start-page: 244 year: 2014 ident: 10.1016/j.jvoice.2020.05.029_bib0007 article-title: Automatic system to detect the type of voice pathology publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2014.03.036 – volume: 89 start-page: 171 year: 2016 ident: 10.1016/j.jvoice.2020.05.029_bib0033 article-title: A sparse auto-encoder-based deep neural network approach for induction motor faults classification publication-title: Measurement doi: 10.1016/j.measurement.2016.04.007 – volume: 313 start-page: 504 year: 2006 ident: 10.1016/j.jvoice.2020.05.029_bib0018 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 – volume: 345 start-page: 340 year: 2016 ident: 10.1016/j.jvoice.2020.05.029_bib0020 article-title: Deep learning approach for active classification of electrocardiogram signals publication-title: Inf Sci doi: 10.1016/j.ins.2016.01.082 – volume: 57 start-page: 1199 year: 2019 ident: 10.1016/j.jvoice.2020.05.029_bib0027 article-title: Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury publication-title: Med Biol Eng Comput doi: 10.1007/s11517-019-01949-4 – volume: 11 start-page: 1 year: 2014 ident: 10.1016/j.jvoice.2020.05.029_bib0001 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: 18 start-page: 290 year: 2018 ident: 10.1016/j.jvoice.2020.05.029_bib0029 article-title: Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework publication-title: Neuroimage Clin doi: 10.1016/j.nicl.2018.01.032 – volume: 428 start-page: 49 year: 2018 ident: 10.1016/j.jvoice.2020.05.029_bib0021 article-title: Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction publication-title: Inf Sci doi: 10.1016/j.ins.2017.10.044 |
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| SubjectTerms | Acoustics Automatic classification Deep neural network Humans Mel frequency cepstral coefficients Neural Networks, Computer Otolaryngology Pathological voice Stacked sparse autoencoder Support Vector Machine Voice |
| Title | Deep Neural Network for Automatic Classification of Pathological Voice Signals |
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