The nature of statistical learning theory
The nature of statistical learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparative study on content-based music genre classification
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
From dynamic classifier selection to dynamic ensemble selection
Pattern Recognition
Selection of Training Instances for Music Genre Classification
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Combining Visual and Acoustic Features for Music Genre Classification
ICMLA '11 Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops - Volume 02
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In this paper we present an approach to music genre classification which converts an audio signal into spectrograms and extracts texture features from these time-frequency images which are then used for modeling music genres in a classification system. The texture features are based on Local Binary Pattern, a structural texture operator that has been successful in recent image classification research. Experiments are performed with two well-known datasets: the Latin Music Database (LMD), and the ISMIR 2004 dataset. The proposed approach takes into account some different zoning mechanisms to perform local feature extraction. Results obtained with and without local feature extraction are compared. We compare the performance of texture features with that of commonly used audio content based features (i.e. from the MARSYAS framework), and show that texture features always outperforms the audio content based features. We also compare our results with results from the literature. On the LMD, the performance of our approach reaches about 82.33%, above the best result obtained in the MIREX 2010 competition on that dataset. On the ISMIR 2004 database, the best result obtained is about 80.65%, i.e. below the best result on that dataset found in the literature.