# 1D CNN Architectures for Music Genre Classification

arXiv: https://arxiv.org/abs/2105.07302 · HTML: https://mlsanity.com/paper/2105.07302/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2021.

## Built on by (most cited first, 5 of 5)

- [The Scattering Transform Network with Generalized Morse Wavelets and Its Application to Music Genre Classification](https://mlsanity.com/paper/2206.07857/) (2022)
- [Deep Insights into Cognitive Decline: A Survey of Leveraging Non-Intrusive Modalities with Deep Learning Techniques](https://mlsanity.com/paper/2410.18972/) (2024)
- [Gamified AI Approch for Early Detection of Dementia](https://mlsanity.com/paper/2405.16538/) (2024)
- [Music Genre Classification using Large Language Models](https://mlsanity.com/paper/2410.08321/) (2024)
- [Demystifying ChatGPT: How It Masters Genre Recognition](https://mlsanity.com/paper/2507.03875/) (2025)

## Abstract (arXiv)

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.