http://www.cnr.it/ontology/cnr/individuo/prodotto/ID222961
Audio stream classification for multimedia database search (Contributo in atti di convegno)
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- Audio stream classification for multimedia database search (Contributo in atti di convegno) (literal)
- Anno
- 2013-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1117/12.2006478 (literal)
- Alternative label
MT. Artese *; S. Bianco** ; I. Gagliardi *and F. Gasparini ** (2013)
Audio stream classification for multimedia database search
in Proc. SPIE 8667, Multimedia Content and Mobile Devices, 86670G, Burlingame, California, USA, February 03, 2013
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- MT. Artese *; S. Bianco** ; I. Gagliardi *and F. Gasparini ** (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1662459 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
- Multimedia Content and Mobile Devices (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
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- Scopu (literal)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- *ITC - CNR
**Università di Milano Bicocca DISCO Dipartimento di informatica sistemistica e comunicazione (literal)
- Titolo
- Audio stream classification for multimedia database search (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
- Cees G. M. Snoek; Lyndon S. Kennedy; Reiner Creutzburg; David Akopian; Dietmar Wüller; Kevin J. Matherson; Todor G. Georgiev; Andrew Lumsdaine (literal)
- Abstract
- Search and retrieval of huge archives of Multimedia data is a challenging task. A classification step is often used to reduce the number of entries on which to perform the subsequent search. In particular, when new entries of the database are continuously added, a fast classification based on simple threshold evaluation is desirable. In this work we present a CART-based (Classification And Regression Tree [1]) classification framework for audio streams belonging to multimedia databases. The database considered is the Archive of Ethnography and Social History (AESS) [2], which is mainly composed of popular songs and other audio records describing the popular traditions handed down generation by generation, such as traditional fairs, and customs. The peculiarities of this database are that it is continuously updated; the audio recordings are acquired in unconstrained environment; and for the non-expert human user is difficult to create the ground truth labels. In our experiments, half of all the available audio files have been randomly extracted and used as training set. The remaining ones have been used as test set. The classifier has been trained to distinguish among three different classes: speech, music, and song. All the audio files in the dataset have been previously manually labeled into the three classes above defined by domain experts. (literal)
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