  In music domain, feature learning has been conducted mainly in two ways:
unsupervised learning based on sparse representations or supervised learning by
semantic labels such as music genre. However, finding discriminative features
in an unsupervised way is challenging and supervised feature learning using
semantic labels may involve noisy or expensive annotation. In this paper, we
present a supervised feature learning approach using artist labels annotated in
every single track as objective meta data. We propose two deep convolutional
neural networks (DCNN) to learn the deep artist features. One is a plain DCNN
trained with the whole artist labels simultaneously, and the other is a Siamese
DCNN trained with a subset of the artist labels based on the artist identity.
We apply the trained models to music classification and retrieval tasks in
transfer learning settings. The results show that our approach is comparable to
previous state-of-the-art methods, indicating that the proposed approach
captures general music audio features as much as the models learned with
semantic labels. Also, we discuss the advantages and disadvantages of the two
models.
