Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Construction and Evaluation of a Robust Multifeature Speech/Music Discriminator
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Hierarchical classification of audio data for archiving and retrieving
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Speech/music discrimination for multimedia applications
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
Protein Disordered Region Prediction by SVM with Post-Processing
CISIS '08 Proceedings of the 2008 International Conference on Complex, Intelligent and Software Intensive Systems
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
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In our previous work [1], a Speech/Music classifier is proposed on the basis of the feature subset selection (FSS) tool and oblique decision tree induced by the algorithm OC1. In this paper, we endeavor to improve it by State transfer (ST) strategy whose aim is to refine the classification results, according to the fact that adjacent segments in one audio file have strong relevance to each other. The proposed algorithm is evaluated by a set of 5-to-11-minute 504 audio files of different types of speech and music in three Signal-to-Noise Ratio (SNR) levels: 30dB, 20dB and 10dB. The results show that ST strategy averagely improves the accuracy for music by 3.3% at 10 dB and 2.3% at 20 dB while keeping accuracy rate of speech almost unchanged. The speech classification rate is also lifted by 5.7% at 10dB on average.