Title: Transfer learning for music classification and regression tasks,
Abstract: In this paper, we present a transfer learning approach for music
classification and regression tasks. We propose to use a pre-trained convnet
feature, a concatenated feature vector using the activations of feature maps of
multiple layers in a trained convolutional network. We show how this convnet
feature can serve as general-purpose music representation. In the experiments,
a convnet is trained for music tagging and then transferred to other
music-related classification and regression tasks. The convnet feature
outperforms the baseline MFCC feature in all the considered tasks and several
previous approaches that are aggregating MFCCs as well as low- and high-level
music features.