Music Genre Classification with Paralleling Recurrent Convolutional Neural Network

TL;DR

This paper introduces a hybrid deep learning architecture combining paralleling CNN and Bi-RNN blocks to improve music genre classification by capturing both spatial features and temporal sequences, resulting in enhanced performance.

Contribution

The paper proposes a novel hybrid architecture that integrates paralleling CNN and Bi-RNN for more effective music genre classification, addressing limitations of previous models.

Findings

Improved classification accuracy over existing methods

The Bi-RNN complements CNN features effectively

Fusion of spatial and temporal features enhances robustness

Abstract

Deep learning has been demonstrated its effectiveness and efficiency in music genre classification. However, the existing achievements still have several shortcomings which impair the performance of this classification task. In this paper, we propose a hybrid architecture which consists of the paralleling CNN and Bi-RNN blocks. They focus on spatial features and temporal frame orders extraction respectively. Then the two outputs are fused into one powerful representation of musical signals and fed into softmax function for classification. The paralleling network guarantees the extracting features robust enough to represent music. Moreover, the experiments prove our proposed architecture improve the music genre classification performance and the additional Bi-RNN block is a supplement for CNNs.