Deep Image Features in Music Information Retrieval

Applications of Convolutional Neural Networks (CNNs) to various problems have been the subject of a number of recent studies ranging from image classification and object detection to scene parsing, segmentation 3D volumetric images and action recognition in videos. CNNs are able to learn input data representation, instead of using fixed engineered features. In this study, the image model trained on CNN were applied to a Music Information Retrieval (MIR), in particular to musical genre recognition. The model was trained on ILSVRC-2012 (more than 1 million natural images) to perform image classification and was reused to perform genre classification using spectrograms images. Harmonic/percussive separation was applied, because it is characteristic for musical genre. At final stage, the evaluation of various strategies of merging Support Vector Machines (SVMs) was performed on well known in MIR community GTZAN dataset. Even though, the model was trained on natural images, the results achieved in this study were close to the state-of-the-art. Keywords—music information retrieval, deep learning, genre classification, convolutional neural networks, transfer learning