Abstract

Music genre is an important feature to identify a musical work. Thus, it is the most used label to organize musical datasets. However, this label is not always available and its identification is not a simple and direct task. Hence, in literature we can find many music genre classification (MGC) methods, with a variety of features and machine learning algorithms (MLA). In this paper, we propose an MGC system by using two levels of hierarchical mining, GLCM (gray level co-occurrence matrix) networks generated from the mel-spectrogram and a multi-hybrid feature strategy. Three types of complex networks were generated: GLCM network Gg, Superpixels network Gs, and GLCM network of each node of Gs (Ggsi network). The multi-hybrid features are formed by textural and topological measures of complex networks and acoustic measures. In the classification step, we used three datasets: GTZAN, Homburg, and ISMIR; two MLAs belonging to the classifier ensemble approach, and (10)-fold cross-validation repeated 100 times. Several experiments were performed using feature combinations of macro-mining (global features of Gg and Gs) and micro-mining (global features of Ggsi). For GTZAN, we performed a detailed analysis of individual class performance and calculated our new ranking logarithmic score (RLS) applied to the F1-score. For all datasets, the RLS and accuracy values were compared with several state-of-the-art methods. The accuracy obtained using micro-mining was >90%, which reveals a satisfactory result.

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