Convolutional Recurrent Neural Networks for Music Classification
Authors:
- Keunwoo ChoiKeunwoo Choi
- George FazekasGFGeorge Fazekas
- Kyunghyun ChoKyunghyun Cho
Preprint
Abstract
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 two 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 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.Keywords
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This publication has 2 references indexed in Scilit:
- An End-to-End Neural Network for Polyphonic Piano Music TranscriptionIEEE/ACM Transactions on Audio, Speech, and Language Processing, 2016
- On the Properties of Neural Machine Translation: Encoder–Decoder Approaches2014