| Paper: | MLSP-P4.8 | ||
| Session: | Audio and Communication Applications | ||
| Time: | Thursday, May 18, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Speech and Audio Processing Applications | ||
| Title: | A Speech/music Discriminator for Radio Recordings Using Bayesian Networks | ||
| Authors: | Theodoros Giannakopoulos, Aggelos Pikrakis, Sergios Theodoridis, University of Athens, Greece | ||
| Abstract: | This paper presents a speech/music discriminator for radio recordings. The segmentation stage is based on the detection of changes in the energy distribution of the audio signal. For the classification stage, Bayesian Networks have been adopted in order to combine the results of nine k-Nearest Neighbor classifiers trained on individual features. To this end, a comparison of the performance of three popular Bayesian Network architectures is presented. Furthermore, in order to reduce the number of features used for classification, a new feature selection scheme is introduced, that is also based on the properties of Bayesian Networks. The proposed system has been tested on real Internet broadcasts of BBC radio stations. | ||
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