Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Audio data retrieval and recognition using model selection criterion

: Biatov, K.; Hesseler, W.; Köhler, J.

Fulltext urn:nbn:de:0011-n-870930 (280 KByte PDF)
MD5 Fingerprint: 22ec9d96aff608f05450f306ea4b09f7
© 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Created on: 13.1.2009

Wysocki, B.J. ; Institute of Electrical and Electronics Engineers -IEEE-:
2nd International Conference on Signal Processing and Communication Systems, ICSPCS 2008. CD-ROM : DSPCS 2008, WITSP 2008, 15-17 December 2008, Gold Coast, Australia
New York, NY: IEEE, 2008
ISBN: 978-0-9756934-6-9
5 pp.
International Conference on Signal Processing and Communication Systems (ICSPCS) <2, 2008, Gold Coast>
International Symposium on DSP and Communication Systems (DSPCS) <10, 2008, Gold Coast>
Workshop on the Internet, Telecommunications and Signal Processing (WITSP) <7, 2008, Gold Coast>
Conference Paper, Electronic Publication
Fraunhofer IAIS ()
model selection criterion; Bayesian Information Criterion; direct audio search; environmental sound; Latent Semantic Indexing

Model selection criterion is an unsupervised technique that can be used to compare statistical distributions of the data. In this paper a new application of model selection criterion are presented. The technique is applied for direct audio search in German broadcast news with the high variability in duration and loudness of the search patterns. Experiments for identification of 14 environmental sounds are carried out using model selection criterion as distance metric. For environmental sounds detection the decision is based on mutual similarity of compared events to the set of reference events. For audio events recognition Latent Semantic Indexing (LSI) is also tested. Approximately 500 audio segments from 14 sound types are used in the recognition test. The experiments show that the applications of model selection criterion for direct audio search, unsupervised environmental sounds analysis and sounds recognition using LSI are effective and accurate.