Comparison of Two Speech/Music Segmentation Systems For Audio Indexing on the Web
Identifieur interne : 008315 ( Main/Merge ); précédent : 008314; suivant : 008316Comparison of Two Speech/Music Segmentation Systems For Audio Indexing on the Web
Auteurs : Joseph Razik [France] ; Christine Sénac [France] ; Dominique Fohr [France] ; Odile Mella [France] ; Nathalie Parlangeau-Vallès [France]Source :
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Abstract
This article talks about two majors ways of performing a speech/music segmentation task. The first one uses a competing modelling approach based on classical speech recognition parameters (MFCC). The second one uses a class/non-class approach for both main topics: speech/non-speech and music/non-music. In order to fit closely speech and music characteristics, different kinds of parameters are used, MFCC and spectral coefficients. We present both approaches with some intrinsic experiments. Then, we compare their speech/music discrimination accuracy using a real-world testing corpus: a broadcast program containing noisy interviews, superimposed segments (speech with music), and an alternation of broad-band speech and telephone speech. Within the classical approach, we can notice that either the derivative alone, or the second derivative alone, plays a major role in the discrimination process as well as the number of cepstral coefficients. In the differentiated way, the class/non-class approach is more homogeneous.
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type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author><author><name sortKey="Parlangeau Valles, Nathalie" sort="Parlangeau Valles, Nathalie" uniqKey="Parlangeau Valles N" first="Nathalie" last="Parlangeau-Vallès">Nathalie Parlangeau-Vallès</name><affiliation wicri:level="1"><hal:affiliation type="researchteam" xml:id="struct-2359" status="OLD"><idno type="RNSR">200118295L</idno><orgName>Analysis, perception and recognition of speech</orgName><orgName type="acronym">PAROLE</orgName><desc><address><country key="FR"></country></address><ref 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type="acronym">Inria</orgName><desc><address><addrLine>Domaine de VoluceauRocquencourt - BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-300291" type="indirect"><org type="institution" xml:id="struct-300291" status="OLD"><orgName>Université Henri Poincaré - Nancy 1</orgName><orgName type="acronym">UHP</orgName><date type="end">2011-12-31</date><desc><address><addrLine>24-30 rue Lionnois, BP 60120, 54 003 NANCY cedex, France</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300292" type="indirect"><org type="institution" xml:id="struct-300292" status="OLD"><orgName>Université Nancy 2</orgName><date type="end">2011-12-31</date><desc><address><addrLine>91 avenue de la Libération, BP 454, 54001 Nancy cedex</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300293" type="indirect"><org type="institution" xml:id="struct-300293" status="OLD"><orgName>Institut National Polytechnique de Lorraine</orgName><orgName type="acronym">INPL</orgName><date type="end">2011-12-31</date><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-2496" type="direct"><org type="laboratory" xml:id="struct-2496" status="OLD"><orgName>INRIA Lorraine</orgName><desc><address><addrLine>615 rue du Jardin Botanique 54600 Villers-lès-Nancy</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/centre-de-recherche-inria/nancy-grand-est</ref></desc><listRelation><relation active="#struct-300009" type="direct"></relation></listRelation></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>audio indexing</term><term>gaussian mixture models</term><term>speech/music segmentation</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This article talks about two majors ways of performing a speech/music segmentation task. The first one uses a competing modelling approach based on classical speech recognition parameters (MFCC). The second one uses a class/non-class approach for both main topics: speech/non-speech and music/non-music. In order to fit closely speech and music characteristics, different kinds of parameters are used, MFCC and spectral coefficients. We present both approaches with some intrinsic experiments. Then, we compare their speech/music discrimination accuracy using a real-world testing corpus: a broadcast program containing noisy interviews, superimposed segments (speech with music), and an alternation of broad-band speech and telephone speech. Within the classical approach, we can notice that either the derivative alone, or the second derivative alone, plays a major role in the discrimination process as well as the number of cepstral coefficients. In the differentiated way, the class/non-class approach is more homogeneous.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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