1D CNN Architectures for Music Genre Classification

TL;DR

This paper introduces a 1D residual CNN architecture for music genre classification directly from raw audio, demonstrating improved accuracy over other 1D CNN models on a public dataset.

Contribution

The paper presents a novel 1D residual CNN architecture that effectively classifies music genres from raw audio signals, outperforming existing 1D CNN models.

Findings

Achieves 80.93% mean accuracy on a public dataset.

Outperforms other recent 1D CNN architectures.

Effective segmentation improves classification accuracy.

Abstract

This paper proposes a 1D residual convolutional neural network (CNN) architecture for music genre classification and compares it with other recent 1D CNN architectures. The 1D CNNs learn a representation and a discriminant directly from the raw audio signal. Several convolutional layers capture the time-frequency characteristics of the audio signal and learn various filters relevant to the music genre recognition task. The proposed approach splits the audio signal into overlapped segments using a sliding window to comply with the fixed-length input constraint of the 1D CNNs. As a result, music genre classification can be carried out on a single audio segment or on the aggregation of the predictions on several audio segments, which improves the final accuracy. The performance of the proposed 1D residual CNN is assessed on a public dataset of 1,000 audio clips. The experimental results have shown that it achieves 80.93% of mean accuracy in classifying music genres and outperforms other 1D CNN architectures.