Robustness of Auditory Teager Energy Cepstrum Coefficients for Classification of Pathological and Normal Voices in Noisy Environments
Identifieur interne : 000202 ( Main/Exploration ); précédent : 000201; suivant : 000203Robustness of Auditory Teager Energy Cepstrum Coefficients for Classification of Pathological and Normal Voices in Noisy Environments
Auteurs : Lotfi Salhi [Tunisie] ; Adnane Cherif [Tunisie]Source :
- The Scientific World Journal [ 2356-6140 ] ; 2013.
Descripteurs français
- KwdFr :
- Adulte (MeSH), Adulte d'âge moyen (MeSH), Algorithmes (MeSH), Bruit (MeSH), Diagnostic assisté par ordinateur (méthodes), Femelle (MeSH), Humains (MeSH), Logiciel de reconnaissance de la parole (MeSH), Mâle (MeSH), Qualité de la voix (MeSH), Reconnaissance automatique des formes (méthodes), Reproductibilité des résultats (MeSH), Sensibilité et spécificité (MeSH), Spectrographie sonore (méthodes), Sujet âgé (MeSH), Sujet âgé de 80 ans ou plus (MeSH), Troubles de la voix (diagnostic), Troubles de la voix (physiopathologie).
- MESH :
- diagnostic : Troubles de la voix.
- méthodes : Diagnostic assisté par ordinateur, Reconnaissance automatique des formes, Spectrographie sonore.
- physiopathologie : Troubles de la voix.
- Adulte, Adulte d'âge moyen, Algorithmes, Bruit, Femelle, Humains, Logiciel de reconnaissance de la parole, Mâle, Qualité de la voix, Reproductibilité des résultats, Sensibilité et spécificité, Sujet âgé, Sujet âgé de 80 ans ou plus.
English descriptors
- KwdEn :
- Adult (MeSH), Aged (MeSH), Aged, 80 and over (MeSH), Algorithms (MeSH), Diagnosis, Computer-Assisted (methods), Female (MeSH), Humans (MeSH), Male (MeSH), Middle Aged (MeSH), Noise (MeSH), Pattern Recognition, Automated (methods), Reproducibility of Results (MeSH), Sensitivity and Specificity (MeSH), Sound Spectrography (methods), Speech Recognition Software (MeSH), Voice Disorders (diagnosis), Voice Disorders (physiopathology), Voice Quality (MeSH).
- MESH :
- diagnosis : Voice Disorders.
- methods : Diagnosis, Computer-Assisted, Pattern Recognition, Automated, Sound Spectrography.
- physiopathology : Voice Disorders.
- Adult, Aged, Aged, 80 and over, Algorithms, Female, Humans, Male, Middle Aged, Noise, Reproducibility of Results, Sensitivity and Specificity, Speech Recognition Software, Voice Quality.
Abstract
This paper focuses on a robust feature extraction algorithm for automatic classification of pathological and normal voices in noisy environments. The proposed algorithm is based on human auditory processing and the nonlinear Teager-Kaiser energy operator. The robust features which labeled Teager Energy Cepstrum Coefficients (TECCs) are computed in three steps. Firstly, each speech signal frame is passed through a Gammatone or Mel scale triangular filter bank. Then, the absolute value of the Teager energy operator of the short-time spectrum is calculated. Finally, the discrete cosine transform of the log-filtered Teager Energy spectrum is applied. This feature is proposed to identify the pathological voices using a developed neural system of multilayer perceptron (MLP). We evaluate the developed method using mixed voice database composed of recorded voice samples from normophonic or dysphonic speakers. In order to show the robustness of the proposed feature in detection of pathological voices at different White Gaussian noise levels, we compare its performance with results for clean environments. The experimental results show that TECCs computed from Gammatone filter bank are more robust in noisy environments than other extracted features, while their performance is practically similar to clean environments.
Url:
DOI: 10.1155/2013/435729
PubMed: 23818821
PubMed Central: 3681261
Affiliations:
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Le document en format XML
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last="Salhi">Lotfi Salhi</name><affiliation wicri:level="1"><nlm:aff id="I1">Signal Processing Laboratory, Physics Department, Sciences Faculty of Tunis, University of Tunis ElManar, 1060 Tunis, Tunisia</nlm:aff><country xml:lang="fr">Tunisie</country><wicri:regionArea>Signal Processing Laboratory, Physics Department, Sciences Faculty of Tunis, University of Tunis ElManar, 1060 Tunis</wicri:regionArea></affiliation></author><author><name sortKey="Cherif, Adnane" sort="Cherif, Adnane" uniqKey="Cherif A" first="Adnane" last="Cherif">Adnane Cherif</name><affiliation wicri:level="1"><nlm:aff id="I1">Signal Processing Laboratory, Physics Department, Sciences Faculty of Tunis, University of Tunis ElManar, 1060 Tunis, Tunisia</nlm:aff><country xml:lang="fr">Tunisie</country><wicri:regionArea>Signal Processing Laboratory, Physics Department, Sciences Faculty of Tunis, University of Tunis ElManar, 1060 Tunis</wicri:regionArea></affiliation></author></analytic><series><title level="j">The Scientific World Journal</title><idno type="ISSN">2356-6140</idno><idno type="eISSN">1537-744X</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult (MeSH)</term><term>Aged (MeSH)</term><term>Aged, 80 and over (MeSH)</term><term>Algorithms (MeSH)</term><term>Diagnosis, Computer-Assisted (methods)</term><term>Female (MeSH)</term><term>Humans (MeSH)</term><term>Male (MeSH)</term><term>Middle Aged (MeSH)</term><term>Noise (MeSH)</term><term>Pattern Recognition, Automated (methods)</term><term>Reproducibility of Results (MeSH)</term><term>Sensitivity and Specificity (MeSH)</term><term>Sound Spectrography (methods)</term><term>Speech Recognition Software (MeSH)</term><term>Voice Disorders (diagnosis)</term><term>Voice Disorders (physiopathology)</term><term>Voice Quality (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte (MeSH)</term><term>Adulte d'âge moyen (MeSH)</term><term>Algorithmes (MeSH)</term><term>Bruit (MeSH)</term><term>Diagnostic assisté par ordinateur (méthodes)</term><term>Femelle (MeSH)</term><term>Humains (MeSH)</term><term>Logiciel de reconnaissance de la parole (MeSH)</term><term>Mâle (MeSH)</term><term>Qualité de la voix (MeSH)</term><term>Reconnaissance automatique des formes (méthodes)</term><term>Reproductibilité des résultats (MeSH)</term><term>Sensibilité et spécificité (MeSH)</term><term>Spectrographie sonore (méthodes)</term><term>Sujet âgé (MeSH)</term><term>Sujet âgé de 80 ans ou plus (MeSH)</term><term>Troubles de la voix (diagnostic)</term><term>Troubles de la voix (physiopathologie)</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Voice Disorders</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Troubles de la voix</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Diagnosis, 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moyen</term><term>Algorithmes</term><term>Bruit</term><term>Femelle</term><term>Humains</term><term>Logiciel de reconnaissance de la parole</term><term>Mâle</term><term>Qualité de la voix</term><term>Reproductibilité des résultats</term><term>Sensibilité et spécificité</term><term>Sujet âgé</term><term>Sujet âgé de 80 ans ou plus</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>This paper focuses on a robust feature extraction algorithm for automatic classification of pathological and normal voices in noisy environments. The proposed algorithm is based on human auditory processing and the nonlinear Teager-Kaiser energy operator. The robust features which labeled Teager Energy Cepstrum Coefficients (TECCs) are computed in three steps. Firstly, each speech signal frame is passed through a Gammatone or Mel scale triangular filter bank. Then, the absolute value of the Teager energy operator of the short-time spectrum is calculated. Finally, the discrete cosine transform of the log-filtered Teager Energy spectrum is applied. This feature is proposed to identify the pathological voices using a developed neural system of multilayer perceptron (MLP). We evaluate the developed method using mixed voice database composed of recorded voice samples from normophonic or dysphonic speakers. In order to show the robustness of the proposed feature in detection of pathological voices at different White Gaussian noise levels, we compare its performance with results for clean environments. The experimental results show that TECCs computed from Gammatone filter bank are more robust in noisy environments than other extracted features, while their performance is practically similar to clean environments.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Yu, P" uniqKey="Yu P">P Yu</name></author><author><name sortKey="Ouaknine, M" uniqKey="Ouaknine M">M Ouaknine</name></author><author><name sortKey="Revis, J" uniqKey="Revis J">J Revis</name></author><author><name sortKey="Giovanni, A" uniqKey="Giovanni A">A Giovanni</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Boyanov, B" uniqKey="Boyanov B">B Boyanov</name></author><author><name sortKey="Hadjitodorov, S" uniqKey="Hadjitodorov S">S Hadjitodorov</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Parsa, V" uniqKey="Parsa V">V Parsa</name></author><author><name sortKey="Jamieson, Dg" uniqKey="Jamieson 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Yegnanarayana</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Jabloun, F" uniqKey="Jabloun F">F Jabloun</name></author><author><name sortKey="Cetin, Ae" uniqKey="Cetin A">AE Çetin</name></author><author><name sortKey="Erzin, E" uniqKey="Erzin E">E Erzin</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kaiser, Jf" uniqKey="Kaiser J">JF Kaiser</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Patil, Ha" uniqKey="Patil H">HA Patil</name></author><author><name sortKey="Parhi, Kk" uniqKey="Parhi K">KK Parhi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kaiser, Jf" uniqKey="Kaiser J">JF Kaiser</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Dimitriadis, D" uniqKey="Dimitriadis D">D Dimitriadis</name></author><author><name sortKey="Maragos, P" uniqKey="Maragos P">P Maragos</name></author><author><name sortKey="Potamianos, A" uniqKey="Potamianos A">A Potamianos</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Tunisie</li></country></list><tree><country name="Tunisie"><noRegion><name sortKey="Salhi, Lotfi" sort="Salhi, Lotfi" uniqKey="Salhi L" first="Lotfi" last="Salhi">Lotfi Salhi</name></noRegion><name sortKey="Cherif, Adnane" sort="Cherif, Adnane" uniqKey="Cherif A" first="Adnane" last="Cherif">Adnane Cherif</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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