Albayzín-2014 evaluation: audio segmentation and classification in broadcast news domains
Entidad
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesEditor
Springer International PublishingFecha de edición
2015-12-01Cita
10.1186/s13636-015-0076-3
Eurasip Journal on Audio, Speech, and Music Processing 2015.1 (2015): 33
ISSN
1687-4722DOI
10.1186/s13636-015-0076-3Financiado por
This work has been partially funded by the Spanish Government and the European Union (FEDER) under the project TIN2011-28169-C05-02 and supported by the European Regional Development Fund and the Spanish Government (‘SpeechTech4All Project’ TEC2012-38939-C03)Proyecto
Gobierno de España. TIN2011-28169-C05-02; Gobierno de España. TEC2012-38939-C03Versión del editor
http://dx.doi.org/10.1186/s13636-015-0076-3Materias
Albayzín-2014 evaluation; Audio segmentation; Broadcast news; TelecomunicacionesNota
The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1186/s13636-015-0076-3Resumen
Audio segmentation is important as a pre-processing task to improve the performance of many speech technology tasks and, therefore, it has an undoubted research interest. This paper describes the database, the metric, the systems and the results for the Albayzín-2014 audio segmentation campaign. In contrast to previous evaluations where the task was the segmentation of non-overlapping classes, Albayzín-2014 evaluation proposes the delimitation of the presence of speech, music and/or noise that can be found simultaneously. The database used in the evaluation was created by fusing different media and noises in order to increase the difficulty of the task. Seven segmentation systems from four different research groups were evaluated and combined. Their experimental results were analyzed and compared with the aim of providing a benchmark and showing up the promising directions in this field.
Lista de ficheros
Google Scholar:Castán, Diego
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Tavárez, David E.
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Lopez-Otero, Paula
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Franco-Pedroso, Javier
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Delgado, Héctor
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Navas, Eva
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Docío-Fernández, Laura
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Ramos Castro, Daniel
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Serrano, Javier
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Ortega de la Puente, Alfonso
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Lleida, Eduardo
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