The nature of statistical learning theory
The nature of statistical learning theory
A robust audio classification and segmentation method
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Linear Prediction of Speech
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
Real-time discrimination of broadcast speech/music
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Speech/music discrimination for multimedia applications
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
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In this paper, we propose an effective algorithm to automatically identify and discriminate music content. Linear prediction coefficients, zero crossing rates and mel-frequency cepstral coefficients are calculated to characterize music content. Based on calculated features, support vector machines are applied to obtain the optimal class boundaries between vocal music and pure music by learning from training data. Experimental results of support vector machine learning show good performance in music discrimination and are more advantageous than traditional Euclidean distance based method.