The availability of digital media has grown tremendously with the fast-paced ever-growing
storage and communication technologies. As a result, today, we are facing a problem in
indexing and browsing the huge amounts of multimedia data. This amount of data is
impossible to be indexed or browsed by hand so automatic indexing and browsing systems
are proposed. Audio Event Detection is a research area which tries to analyse the audio data
in a semantic and perceptual manner, to bring a conceptual solution to this problem. In this
thesis, a method for detecting several audio events in TV broadcast is proposed. The
proposed method includes an audio segmentation stage to detect event boundaries.
Broadcast audio is classified into 17 classes. The feature set for each event is obtained by
using a feature selection algorithm to select suitable features among a large set of popular
descriptors. Support Vector Machines and Gaussian Mixture Models are used as classifiers
and the proposed system achieved an average recall rate of 88% for 17 different audio
events. Comparing with the results in the literature, the proposed method is promising.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12613724/index.pdf |
| Date | 01 September 2011 |
| Creators | Ozan, Ezgi Can |
| Contributors | Ciloglu, Tolga |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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