  We introduce a convolutional recurrent neural network (CRNN) for music
tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local
feature extraction and recurrent neural networks for temporal summarisation of
the extracted features. We compare CRNN with three CNN structures that have
been used for music tagging while controlling the number of parameters with
respect to their performance and training time per sample. Overall, we found
that CRNNs show a strong performance with respect to the number of parameter
and training time, indicating the effectiveness of its hybrid structure in
music feature extraction and feature summarisation.
