Music genre visualization and classification exploiting a small set of high-level semantic features

In this paper a system for continuous analysis, visualization and classification of musical streams is proposed. The system performs visualization and classification task by means of three high-level, semantic features extracted computing a reduction on a multidimensional low-level feature vector through the usage of Gaussian Mixture Models. The visualization of the semantic characteristics of the audio stream has been implemented by mapping the value of the high-level features on a triangular plot and by assigning to each feature a primary color. In this manner, besides having the representation of musical evolution of the signal, we have also obtained representative colors for each musical part of the analyzed streams. The classification exploits a set of one-against-one threedimensional Support Vector Machines trained on some target genres. The obtained results on visualization and classification tasks are very encouraging: our tests on heterogeneous genre streams have shown the validity of proposed approach