A New Method Based on Deep Belief Networks for Learning Features from Symbolic Music
Qiaoli Huang, Zhixing Huang, Yanhong Yuan, Mei Tian · 2015
As the rapid increase of music data, Music Information Retrieval (MIR) have been receiving increasing attention in both the academic and commercial spheres. Feature extraction is a crucial part of many Music Information Retrieval (MIR) tasks. In recent years, deep learning approaches have gained significant interest as a way of learning a higher abstract representation from unlabeled data. In this paper, we present a system that can automatically extract relevant from symbolic music data. Firstly, The lower level features are extracted by using toolbox Music21, the higher level feature are then learned by a Deep Belief Network (DBN), finally the activations of the trained network as inputs for a non-linear Support Vector Machine (SVM) classifier. The experiment results demonstrate that the learned features obtain a better classification accuracy than other classical methods.