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Analysis of the influence of signal compression techniques for voice disorder detection through filter-banked based features

dc.contributor.authorSepúlveda Cano, Lina María
dc.contributor.authorQuiza Montealegre, Jhon Jair
dc.contributor.authorGómez García, Jorge Andrés
dc.date.accessioned2017-06-29T22:22:40Z
dc.date.available2017-06-29T22:22:40Z
dc.date.created2017-06-30
dc.identifier.issn1692-3324
dc.identifier.urihttp://hdl.handle.net/11407/3604
dc.description.abstractEn este artículo se comparan los resultados de utilizar señales de voz comprimidas frente a señales de voz sin comprimir para detectar de forma automática anomalías vocales. Las técnicas de codificación y compresión de voz usadas en este estudio son las mismas que se utilizan de forma estándar en los sistemas de telefonía fija, móvil e IP, y las técnicas de caracterización y clasificación usadas también están dentro de las más utilizadas para la detección automática de anomalías de voz. Los resultados obtenidos permiten concluir que es posible utilizar señales de voz comprimidas para detección automática de patologías vocales sin detrimento en el porcentaje de acierto en el diagnóstico, lo que haría posible la implementación de sistemas de telediagnóstico automático de patologías vocales.spa
dc.description.abstractThis paper compares the results of using compressed voice signals versus uncompressed speech signals to automatically detect voice abnormalities. Coding techniques and voice compression used in this study are the same as those used by default in the fixed, mobile and ip telephony systems, and techniques of characterization and classification used are also among the most used for detecting automatic speech abnormalities. The results obtained indicate that it is possible to use compressed voice signals for automatic detection of vocal pathologies without compromising the success rate in the diagnosis, which would make the implementation of automatic remote diagnosis of vocal pathologies possible.spa
dc.format.extentp. 49-66spa
dc.format.mediumElectrónicospa
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.publisherUniversidad de Medellínspa
dc.relation.urihttp://revistas.udem.edu.co/index.php/ingenierias/article/view/1511
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.sourceRevista Ingenierías Universidad de Medellín; Vol. 16, núm. 30 (2017); 49-66spa
dc.source2248-4094spa
dc.source1692-3324spa
dc.subjectRemote diagnosticsspa
dc.subjectVoice pathology detectionspa
dc.subjectVoice compressionspa
dc.subjectBiosignals analysisspa
dc.subjectTelediagnósticospa
dc.subjectDetección de patologías de vozspa
dc.subjectCompresión de vozspa
dc.subjectAnálisis de bioseñalesspa
dc.titleAnálisis de la influencia de las técnicas de compresión de voz en la detección de anomalías vocalesspa
dc.titleAnalysis of the influence of signal compression techniques for voice disorder detection through filter-banked based featuresspa
dc.typeArticleeng
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.identifier.doi http://dx.doi.org/10.22395/rium.v16n30a3
dc.relation.citationvolume16
dc.relation.citationissue30
dc.relation.citationstartpage49
dc.relation.citationendpage66
dc.audienceComunidad Universidad de Medellínspa
dc.publisher.facultyFacultad de Ingenieríasspa
dc.coverageLat: 06 15 00 N  degrees minutes  Lat: 6.2500  decimal degreesLong: 075 36 00 W  degrees minutes  Long: -75.6000  decimal degreesspa
dc.publisher.placeMedellínspa
dc.creator.affiliationSepúlveda Cano, Lina María; Universidad de Medellínspa
dc.creator.affiliationQuiza Montealegre, Jhon Jair; Universidad de Medellínspa
dc.creator.affiliationGómez García, Jorge Andrés; Universidad Politécnica de Madridspa
dc.relation.referencesS. Kadambe and P. Srinivasan, "Adaptive wavelets for signal classification and compression," AEU-International Journal of Electronics and Communications, vol. 60, pp. 45-55, 2006.spa
dc.relation.referencesG. Rajesh, et al., "Speech compression using different transform techniques," in Computer and Communication Technology (ICCCT), 2011 2nd International Conference on, 2011, pp. 146-151.spa
dc.relation.referencesI. Otung, Communication engineering principles: Palgrave Macmillan, 2001.spa
dc.relation.referencesB. Sklar, Digital communications vol. 2: Prentice Hall NJ, 2001.spa
dc.relation.referencesT. ITU, "Recommendation G. 711," Pulse Code Modulation (PCM) of voice frequencies, November, 1988.spa
dc.relation.referencesR. ITU-T and I. Recommend, "P. 800," Methods for subjective determination of transmission quality, 1996.spa
dc.relation.referencesS. Haykin, Communication systems: John Wiley & Sons, 2008.spa
dc.relation.referencesJ. Davidson, Voice over IP fundamentals: Cisco press, 2006.spa
dc.relation.referencesD. Peña, Análisis de datos multivariantes vol. 24: McGraw-Hill Madrid, 2002.spa
dc.relation.referencesJ. B. Alonso, et al., "Automatic detection of pathologies in the voice by HOS based parameters," EURASIP Journal on Applied Signal Processing, vol. 4, pp. 275-284, 2001.spa
dc.relation.referencesG. Banci, et al., "Vocal fold disorder evaluation by digital speech analysis," Journal of Phonetics, vol. 14, pp. 495-499, 1986.spa
dc.relation.referencesB. Boyanov and S. Hadjitodorov, "Acoustic analysis of pathological voices. A voice analysis system for the screening of laryngeal diseases," Engineering in Medicine and Biology Magazine, IEEE, vol. 16, pp. 74-82, 1997.spa
dc.relation.referencesA. A. Dibazar, et al., "Feature analysis for automatic detection of pathological speech," in Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint, 2002, pp. 182-183 vol.1.spa
dc.relation.referencesM. K. Arjmandi and M. Pooyan, "An optimum algorithm in pathological voice quality assessment using wavelet-packet-based features, linear discriminant analysis and support vector machine," Biomedical Signal Processing and Control, vol. 7, pp. 3-19, 2012.spa
dc.relation.referencesR. J. Moran, et al., "Telephony-based voice pathology assessment using automated speech analysis," Biomedical Engineering, IEEE Transactions on, vol. 53, pp. 468-477, 2006.spa
dc.relation.referencesM. F. Kaleem, et al., "Telephone-quality pathological speech classification using empirical mode decomposition," in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, 2011, pp. 7095-7098.spa
dc.relation.referencesN. Saenz-Lechon, et al., "Effects of Audio Compression in Automatic Detection of Voice Pathologies," Biomedical Engineering, IEEE Transactions on, vol. 55, pp. 2831-2835, 2008.spa
dc.relation.referencesD. Arifianto, "Enhancement of speech over wireless network using sinusoidal modeling and synthesis," in Signal Processing Systems (SiPS), 2013 IEEE Workshop on, 2013, pp. 301-305.spa
dc.relation.referencesV. Uloza, et al., "Exploring the feasibility of smart phone microphone for measurement of acoustic voice parameters and voice pathology screening," European Archives of Oto-Rhino-Laryngology, pp. 1-9, 2015/07/11 2015.spa
dc.relation.referencesM. Eye and E. Infirmary, "Voice disorders database, version. 1.03 (cd-rom)," Lincoln Park, NJ: Kay Elemetrics Corporation, 1994.spa
dc.relation.referencesG. Smillie, Analogue and digital communication techniques: Butterworth-Heinemann, 1999.spa
dc.relation.referencesS. Karapantazis and F.-N. Pavlidou, "VoIP: A comprehensive survey on a promising technology," Computer Networks, vol. 53, pp. 2050-2090, 2009.spa
dc.relation.referencesA. R. Madane, et al., "Speech compression using Linear predictive coding," in proceedings International workshop on Machine Intelligence Research MIR labs, 2009.spa
dc.relation.referencesM. Hasegawa-Johnson, "Lecture notes in speech production, speech coding, and speech recognition," class notes, University of Illinois at Urbana-Champaign, Fall, 2000.spa
dc.relation.referencesL. M. Sepúlveda Cano, "Análisis Dinámico de Relevancia en Bioseñales," Universidad Nacional de Colombia-Sede Manizales, 2013.spa
dc.relation.referencesA. F. Quiceno Manrique, "Análisis tiempo-frecuencia por métodos no paramétricos orientado a la detección de patologías en bioseñales," Universidad Nacional de Colombia-Sede Manizales, 2009.spa
dc.rights.creativecommonsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.identifier.eissn2248-4094
dc.type.coarhttp://purl.org/coar/resource_type/c_6501
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.localArtículo científicospa
dc.type.driverinfo:eu-repo/semantics/article
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellínspa
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellínspa
dc.relation.ispartofjournalRevista Ingenierías Universidad de Medellínspa


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