Audio segmentation of broadcast news : a hierarchical system with feature selection for the  Albayzin-2010 evaluation

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/15469

dc.contributor	Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.contributor	Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
dc.contributor.author	Butko, Taras
dc.contributor.author	Nadeu Camprubí, Climent
dc.date	2011
dc.identifier.citation	Butko, T.; Nadeu, C. Audio segmentation of broadcast news : a hierarchical system with feature selection for the Albayzin-2010 evaluation. A: International Conference on Acoustics, Speech and Signal Processing. "2011 IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings". Barcelona: IEEE Press. Institute of Electrical and Electronics Engineers, 2011, p. 357-360.
dc.identifier.citation	10.1109/ICASSP.2011.5946414
dc.identifier.uri	http://hdl.handle.net/2117/15469
dc.language.iso	eng
dc.publisher	IEEE Press. Institute of Electrical and Electronics Engineers
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic
dc.subject	Hidden Markov models
dc.subject	Audio signal processing
dc.subject	Broadcasting
dc.subject	Model ocult de Markov
dc.subject	Albayzin 2010
dc.subject	So -- Processament de dades
dc.title	Audio segmentation of broadcast news : a hierarchical system with feature selection for the Albayzin-2010 evaluation
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	In this paper, we present an audio segmentation system for broadcast news, and its results in the Albayzin-2010 evaluation. First of all, the Albayzin-2010 evaluation setup, developed by the authors, is presented; in particular, the database and the metric are described. The reported hierarchical HMM-GMM-based system is composed of one binary detector for each of the five considered classes (music, speech, speech over music, speech over noise and other). A fast one-pass-training feature selection technique is adapted to the audio segmentation task to improve the results and to reduce the dimensionality of the input feature vector.
dc.description.abstract	Peer Reviewed

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