Convolutional Recurrent Neural Networks for Music Classification
This work addresses music classification for tagging applications, presenting an incremental improvement by hybridizing existing methods.
The paper tackles music tagging by introducing a convolutional recurrent neural network (CRNN) that combines CNNs for local feature extraction and RNNs for temporal summarization, achieving strong performance with efficient parameter use and training time.
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.