# Music Artist Classification with Convolutional Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1901.04555 · HTML: https://mlsanity.com/paper/1901.04555/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2019.

## Built on by (most cited first, 6 of 6)

- [MovieFactory: Automatic Movie Creation from Text using Large Generative Models for Language and Images](https://mlsanity.com/paper/2306.07257/) (2023)
- [ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Models](https://mlsanity.com/paper/2502.11059/) (2025)
- [A Visual Domain Transfer Learning Approach for Heartbeat Sound Classification](https://mlsanity.com/paper/2107.13237/) (2021)
- [Toward Leveraging Pre-Trained Self-Supervised Frontends for Automatic Singing Voice Understanding Tasks: Three Case Studies](https://mlsanity.com/paper/2306.12714/) (2023)
- [Recurrent and Convolutional Neural Networks in Classification of EEG Signal for Guided Imagery and Mental Workload Detection](https://mlsanity.com/paper/2405.16901/) (2024)
- [Improving Primate Sounds Classification using Binary Presorting for Deep Learning](https://mlsanity.com/paper/2306.16054/) (2023)

## Abstract (arXiv)

Previous attempts at music artist classification use frame level audio features which summarize frequency content within short intervals of time. Comparatively, more recent music information retrieval tasks take advantage of temporal structure in audio spectrograms using deep convolutional and recurrent models. This paper revisits artist classification with this new framework and empirically explores the impacts of incorporating temporal structure in the feature representation. To this end, an established classification architecture, a Convolutional Recurrent Neural Network (CRNN), is applied to the artist20 music artist identification dataset under a comprehensive set of conditions. These include audio clip length, which is a novel contribution in this work, and previously identified considerations such as dataset split and feature level. Our results improve upon baseline works, verify the influence of the producer effect on classification performance and demonstrate the trade-offs between audio length and training set size. The best performing model achieves an average F1 score of 0.937 across three independent trials which is a substantial improvement over the corresponding baseline under similar conditions. Additionally, to showcase the effectiveness of the CRNN's feature extraction capabilities, we visualize audio samples at the model's bottleneck layer demonstrating that learned representations segment into clusters belonging to their respective artists.