Convolutional Recurrent Neural Networks for Music Classification

TL;DR

This paper presents a hybrid convolutional recurrent neural network (CRNN) architecture for music tagging, combining CNNs for local feature extraction and RNNs for temporal summarisation, demonstrating superior efficiency and performance.

Contribution

It introduces a novel CRNN model for music classification that outperforms traditional CNNs in efficiency and effectiveness, with comprehensive comparisons and analysis.

Findings

CRNN achieves better performance with fewer parameters.

CRNN reduces training time compared to CNNs.

Hybrid structure enhances music feature extraction.

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 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.