Music genre recognition based on visual features with dynamic ensemble of classifiers selection

This paper introduces the use of a dynamic ensemble of classifiers selection scheme with a pool of classifiers created to perform automatic music genre classification. The classifiers are based on support vector machine trained with textural features extracted from spectrogram images using Local Binary Patterns. The results obtained on the Latin Music Database showed that local feature extraction and the k-nearest oracle (KNORA) for dynamic ensemble of classifiers selection can reach a recognition rate of 83%, which is a little better than the best result ever reported on this dataset using the restrictions imposed by “artist filter”. In addition, the results are compared with those obtained from traditional approaches using acoustic features.