Audio segmentation-by-classification approach based on factor analysis in broadcast news domain
Resumen: This paper studies a novel audio segmentation-by-classification approach based on factor analysis. The proposed technique compensates the within-class variability by using class-dependent factor loading matrices and obtains the scores by computing the log-likelihood ratio for the class model to a non-class model over fixed-length windows. Afterwards, these scores are smoothed to yield longer contiguous segments of the same class by means of different back-end systems. Unlike previous solutions, our proposal does not make use of specific acoustic features and does not need a hierarchical structure. The proposed method is applied to segment and classify audios coming from TV shows into five different acoustic classes: speech, music, speech with music, speech with noise, and others. The technique is compared to a hierarchical system with specific acoustic features achieving a significant error reduction.
Idioma: Inglés
DOI: 10.1186/s13636-014-0034-5
Año: 2014
Publicado en: EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING 2014, 1 (2014), 1-13
ISSN: 1687-4714

Factor impacto JCR: 0.386 (2014)
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 213 / 247 = 0.862 (2014) - Q4 - T3
Categ. JCR: ACOUSTICS rank: 28 / 30 = 0.933 (2014) - Q4 - T3

Factor impacto SCIMAGO:

Financiación: info:eu-repo/grantAgreement/ES/MINECO/TIN2011-28169-C05-02
Tipo y forma: Article (Published version)
Área (Departamento): Teoría de la Señal y Comunicaciones (Departamento de Ingeniería Electrónica y Comunicaciones)
Exportado de SIDERAL (2017-11-09-11:57:42)

Este artículo se encuentra en las siguientes colecciones:
articulos > articulos-por-area > teoria_de_la_senal_y_comunicaciones



 Notice créée le 2017-11-09, modifiée le 2017-11-09


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