Robustness of Auditory Teager Energy Cepstrum Coefficients for Classification of Pathological and Normal Voices in Noisy Environments
Identifieur interne : 000271 ( Pmc/Checkpoint ); précédent : 000270; suivant : 000272Robustness 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.
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:
Links toward previous steps (curation, corpus...)
Links to Exploration step
PMC:3681261Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Robustness of Auditory Teager Energy Cepstrum Coefficients for Classification of Pathological and Normal Voices in Noisy Environments</title><author><name sortKey="Salhi, Lotfi" sort="Salhi, Lotfi" uniqKey="Salhi L" first="Lotfi" 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></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">23818821</idno><idno type="pmc">3681261</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3681261</idno><idno type="RBID">PMC:3681261</idno><idno type="doi">10.1155/2013/435729</idno><date when="2013">2013</date><idno type="wicri:Area/Pmc/Corpus">000275</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000275</idno><idno type="wicri:Area/Pmc/Curation">000274</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000274</idno><idno type="wicri:Area/Pmc/Checkpoint">000271</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000271</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Robustness of Auditory Teager Energy Cepstrum Coefficients for Classification of Pathological and Normal Voices in Noisy Environments</title><author><name sortKey="Salhi, Lotfi" sort="Salhi, Lotfi" uniqKey="Salhi L" first="Lotfi" 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></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 D">DG Jamieson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Dubuisson, T" uniqKey="Dubuisson T">T Dubuisson</name></author><author><name sortKey="Dutoit, T" uniqKey="Dutoit T">T Dutoit</name></author><author><name sortKey="Gosselin, B" uniqKey="Gosselin B">B Gosselin</name></author><author><name sortKey="Remacle, M" uniqKey="Remacle M">M Remacle</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="D Lessandro, C" uniqKey="D Lessandro C">C d’Alessandro</name></author><author><name sortKey="Bozkurt, B" uniqKey="Bozkurt B">B Bozkurt</name></author><author><name sortKey="Doval, B" uniqKey="Doval B">B Doval</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Boashash, B" uniqKey="Boashash B">B Boashash</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Liu, L" uniqKey="Liu L">L Liu</name></author><author><name sortKey="He, J" uniqKey="He J">J He</name></author><author><name sortKey="Palm, G" uniqKey="Palm G">G Palm</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hegde, Rm" uniqKey="Hegde R">RM Hegde</name></author><author><name sortKey="Murthy, Ha" uniqKey="Murthy H">HA Murthy</name></author><author><name sortKey="Gadde, Vrr" uniqKey="Gadde V">VRR Gadde</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ghitza, O" uniqKey="Ghitza O">O Ghitza</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Glasberg, Br" uniqKey="Glasberg B">BR Glasberg</name></author><author><name sortKey="Moore, Bcj" uniqKey="Moore B">BCJ Moore</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Irino, T" uniqKey="Irino T">T Irino</name></author><author><name sortKey="Patterson, Rd" uniqKey="Patterson R">RD Patterson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kortelainen, J" uniqKey="Kortelainen J">J Kortelainen</name></author><author><name sortKey="Noponen, K" uniqKey="Noponen K">K Noponen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bishop, Cm" uniqKey="Bishop C">CM Bishop</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cherif, A" uniqKey="Cherif A">A Cherif</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Salhi, L" uniqKey="Salhi L">L Salhi</name></author><author><name sortKey="Talbi, M" uniqKey="Talbi M">M Talbi</name></author><author><name sortKey="Abid, S" uniqKey="Abid S">S Abid</name></author><author><name sortKey="Cherif, A" uniqKey="Cherif A">A Cherif</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kacha, A" uniqKey="Kacha A">A Kacha</name></author><author><name sortKey="Grenez, F" uniqKey="Grenez F">F Grenez</name></author><author><name sortKey="Schoentgen, J" uniqKey="Schoentgen J">J Schoentgen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bettens, F" uniqKey="Bettens F">F Bettens</name></author><author><name sortKey="Grenez, F" uniqKey="Grenez F">F Grenez</name></author><author><name sortKey="Schoentgen, J" uniqKey="Schoentgen J">J Schoentgen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Davis, Sb" uniqKey="Davis S">SB Davis</name></author><author><name sortKey="Mermelstein, P" uniqKey="Mermelstein P">P Mermelstein</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Potamianos, A" uniqKey="Potamianos A">A Potamianos</name></author><author><name sortKey="Maragos, P" uniqKey="Maragos P">P Maragos</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Murty, Ksr" uniqKey="Murty K">KSR Murty</name></author><author><name sortKey="Yegnanarayana, B" uniqKey="Yegnanarayana B">B 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><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">ScientificWorldJournal</journal-id><journal-id journal-id-type="iso-abbrev">ScientificWorldJournal</journal-id><journal-id journal-id-type="publisher-id">TSWJ</journal-id><journal-title-group><journal-title>The Scientific World Journal</journal-title></journal-title-group><issn pub-type="ppub">2356-6140</issn><issn pub-type="epub">1537-744X</issn><publisher><publisher-name>Hindawi Publishing Corporation</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">23818821</article-id><article-id pub-id-type="pmc">3681261</article-id><article-id pub-id-type="doi">10.1155/2013/435729</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title>Robustness of Auditory Teager Energy Cepstrum Coefficients for Classification of Pathological and Normal Voices in Noisy Environments</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Salhi</surname><given-names>Lotfi</given-names></name><xref ref-type="aff" rid="I1"></xref><xref ref-type="corresp" rid="cor1">*</xref></contrib><contrib contrib-type="author"><name><surname>Cherif</surname><given-names>Adnane</given-names></name><xref ref-type="aff" rid="I1"></xref></contrib></contrib-group><aff id="I1">Signal Processing Laboratory, Physics Department, Sciences Faculty of Tunis, University of Tunis ElManar, 1060 Tunis, Tunisia</aff><author-notes><corresp id="cor1">*Lotfi Salhi: <email>lotfi.salhi@laposte.net</email></corresp><fn fn-type="other"><p>Academic Editors: E. P. Ong and L. Silva</p></fn></author-notes><pub-date pub-type="collection"><year>2013</year></pub-date><pub-date pub-type="epub"><day>28</day><month>5</month><year>2013</year></pub-date><volume>2013</volume><elocation-id>435729</elocation-id><history><date date-type="received"><day>31</day><month>3</month><year>2013</year></date><date date-type="accepted"><day>8</day><month>5</month><year>2013</year></date></history><permissions><copyright-statement>Copyright © 2013 L. Salhi and A. Cherif.</copyright-statement><copyright-year>2013</copyright-year><license xlink:href="https://creativecommons.org/licenses/by/3.0/"><license-p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><abstract><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></abstract></article-meta></front><floats-group><fig id="fig1" orientation="portrait" position="float"><label>Figure 1</label><caption><p>Block diagram of the proposed system.</p></caption><graphic xlink:href="TSWJ2013-435729.001"></graphic></fig><fig id="fig2" orientation="portrait" position="float"><label>Figure 2</label><caption><p>General schematic of a neural network (MLP).</p></caption><graphic xlink:href="TSWJ2013-435729.002"></graphic></fig><fig id="fig3" orientation="portrait" position="float"><label>Figure 3</label><caption><p>Block diagrams of the extraction of MFCC, MTECC, and GTECC features.</p></caption><graphic xlink:href="TSWJ2013-435729.003"></graphic></fig><fig id="fig4" orientation="portrait" position="float"><label>Figure 4</label><caption><p>Principle of Mel scale filter bank.</p></caption><graphic xlink:href="TSWJ2013-435729.004"></graphic></fig><fig id="fig5" orientation="portrait" position="float"><label>Figure 5</label><caption><p>Electrical and mechanical resonant oscillators.</p></caption><graphic xlink:href="TSWJ2013-435729.005"></graphic></fig><fig id="fig6" orientation="portrait" position="float"><label>Figure 6</label><caption><p>Gammatone function of the cochlear filter.</p></caption><graphic xlink:href="TSWJ2013-435729.006"></graphic></fig><fig id="fig7" orientation="portrait" position="float"><label>Figure 7</label><caption><p>Gammatone filter bank with 25 filters.</p></caption><graphic xlink:href="TSWJ2013-435729.007"></graphic></fig><fig id="fig8" orientation="portrait" position="float"><label>Figure 8</label><caption><p>Feature performance in clean and noisy condition.</p></caption><graphic xlink:href="TSWJ2013-435729.008"></graphic></fig><table-wrap id="tab1" orientation="portrait" position="float"><label>Table 1</label><caption><p>Feature performance in clean and noisy condition.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1">SNR (dB)</th><th align="center" rowspan="1" colspan="1">Clean</th><th align="center" rowspan="1" colspan="1">15</th><th align="center" rowspan="1" colspan="1">10</th><th align="center" rowspan="1" colspan="1">5</th><th align="center" rowspan="1" colspan="1">0</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">MFCC</td><td align="center" rowspan="1" colspan="1"> </td><td align="center" rowspan="1" colspan="1"> </td><td align="center" rowspan="1" colspan="1"> </td><td align="center" rowspan="1" colspan="1"> </td><td align="center" rowspan="1" colspan="1"> </td></tr><tr><td align="left" rowspan="1" colspan="1">CCR<sub>Norm</sub></td><td align="center" rowspan="1" colspan="1">86.76</td><td align="center" rowspan="1" colspan="1">80.52</td><td align="center" rowspan="1" colspan="1">80.22</td><td align="center" rowspan="1" colspan="1">78.59</td><td align="center" rowspan="1" colspan="1">72.06</td></tr><tr><td align="left" rowspan="1" colspan="1">CCR<sub>Path</sub></td><td align="center" rowspan="1" colspan="1">84.13</td><td align="center" rowspan="1" colspan="1">77.32</td><td align="center" rowspan="1" colspan="1">77.36</td><td align="center" rowspan="1" colspan="1">78.29</td><td align="center" rowspan="1" colspan="1">71.43</td></tr><tr><td align="left" rowspan="1" colspan="1">CCR</td><td align="center" rowspan="1" colspan="1">85.45</td><td align="center" rowspan="1" colspan="1">78. 29</td><td align="center" rowspan="1" colspan="1">78.79</td><td align="center" rowspan="1" colspan="1">78.44</td><td align="center" rowspan="1" colspan="1">71.74</td></tr><tr><td align="left" colspan="6" rowspan="1"><hr></hr></td></tr><tr><td align="left" rowspan="1" colspan="1">MTECC</td><td align="center" rowspan="1" colspan="1"> </td><td align="center" rowspan="1" colspan="1"> </td><td align="center" rowspan="1" colspan="1"> </td><td align="center" rowspan="1" colspan="1"> </td><td align="center" rowspan="1" colspan="1"> </td></tr><tr><td align="left" rowspan="1" colspan="1">CCR<sub>Norm</sub></td><td align="center" rowspan="1" colspan="1">88.24</td><td align="center" rowspan="1" colspan="1">86.76</td><td align="center" rowspan="1" colspan="1">87.50</td><td align="center" rowspan="1" colspan="1">86.76</td><td align="center" rowspan="1" colspan="1">85.29</td></tr><tr><td align="left" rowspan="1" colspan="1">CCR<sub>Path</sub></td><td align="center" rowspan="1" colspan="1">88.89</td><td align="center" rowspan="1" colspan="1">87.30</td><td align="center" rowspan="1" colspan="1">85.71</td><td align="center" rowspan="1" colspan="1">84.13</td><td align="center" rowspan="1" colspan="1">84.13</td></tr><tr><td align="left" rowspan="1" colspan="1">CCR</td><td align="center" rowspan="1" colspan="1">88.56</td><td align="center" rowspan="1" colspan="1">87.03</td><td align="center" rowspan="1" colspan="1">86.61</td><td align="center" rowspan="1" colspan="1">85.45</td><td align="center" rowspan="1" colspan="1">84.71</td></tr><tr><td align="left" colspan="6" rowspan="1"><hr></hr></td></tr><tr><td align="left" rowspan="1" colspan="1">GTECC</td><td align="center" rowspan="1" colspan="1"> </td><td align="center" rowspan="1" colspan="1"> </td><td align="center" rowspan="1" colspan="1"> </td><td align="center" rowspan="1" colspan="1"> </td><td align="center" rowspan="1" colspan="1"> </td></tr><tr><td align="left" rowspan="1" colspan="1">CCR<sub>Norm</sub></td><td align="center" rowspan="1" colspan="1">92.65</td><td align="center" rowspan="1" colspan="1">92.65</td><td align="center" rowspan="1" colspan="1">91.18</td><td align="center" rowspan="1" colspan="1">91.18</td><td align="center" rowspan="1" colspan="1">89.71</td></tr><tr><td align="left" rowspan="1" colspan="1">CCR<sub>Path</sub></td><td align="center" rowspan="1" colspan="1">90.48</td><td align="center" rowspan="1" colspan="1">88.89</td><td align="center" rowspan="1" colspan="1">90.48</td><td align="center" rowspan="1" colspan="1">88.89</td><td align="center" rowspan="1" colspan="1">88.89</td></tr><tr><td align="left" rowspan="1" colspan="1">CCR</td><td align="center" rowspan="1" colspan="1">91.56</td><td align="center" rowspan="1" colspan="1">90.77</td><td align="center" rowspan="1" colspan="1">90.83</td><td align="center" rowspan="1" colspan="1">90.03</td><td align="center" rowspan="1" colspan="1">89.30</td></tr></tbody></table></table-wrap></floats-group></pmc><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)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Sante/explor/MaghrebDataLibMedV2/Data/Pmc/Checkpoint
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000271 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Pmc/Checkpoint/biblio.hfd -nk 000271 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Sante
|area= MaghrebDataLibMedV2
|flux= Pmc
|étape= Checkpoint
|type= RBID
|clé= PMC:3681261
|texte= Robustness of Auditory Teager Energy Cepstrum Coefficients for Classification of Pathological and Normal Voices in Noisy Environments
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Pmc/Checkpoint/RBID.i -Sk "pubmed:23818821" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Pmc/Checkpoint/biblio.hfd \
| NlmPubMed2Wicri -a MaghrebDataLibMedV2
| This area was generated with Dilib version V0.6.38. |  |