DocumentCode
2831962
Title
A Speech/Music/Silence/Garbage/ Classifier for Searching and Indexing Broadcast News Material
Author
Patsis, Yorgos ; Verhelst, Werner
Author_Institution
Dept. ETRO, Vrije Univ. Brussel, Brussels
fYear
2008
fDate
1-5 Sept. 2008
Firstpage
585
Lastpage
589
Abstract
An audio classifier that can distinguish between speech, music, silence and garbage has been developed. The classifier was trained and tested on broadcast news material provided by VRT (Flemish Radio and Television Network). Several feature sets and machine learning algorithms have been tested, providing choices of speed and performance for a target system. The audio classifier is part of a greater system that together with visual data can retrieve information from news broadcasts: speech can be converted to text and the speaker can be recognized. Music can be further used for genre classification, jingle recognition or copyright infringement detection. Silence is recognized and used to provide cues on topic changes or speaker turns. At this point everything that is not classified as speech, music or silence is labeled garbage. Garbage classes can be further used for background categorization giving information on the environment where someone speaks (an anchor in the studio or a reporter in the street).
Keywords
audio signal processing; indexing; information retrieval; learning (artificial intelligence); signal classification; audio classifier; background categorization; broadcast news material; indexing; information retrieval; machine learning; searching; speech-music-silence-garbage classifier; Audio databases; Entropy; Expert systems; Frequency; Indexing; Music; Radio broadcasting; Signal detection; Speech processing; TV broadcasting; Audio Classification; Broadcast News;
fLanguage
English
Publisher
ieee
Conference_Titel
Database and Expert Systems Application, 2008. DEXA '08. 19th International Workshop on
Conference_Location
Turin
ISSN
1529-4188
Print_ISBN
978-0-7695-3299-8
Type
conf
DOI
10.1109/DEXA.2008.104
Filename
4624780
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