# Learning to Recognize Musical Genre from Audio

arXiv: https://arxiv.org/abs/1803.05337 · HTML: https://mlsanity.com/paper/1803.05337/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2018.

## Built on by (most cited first, 9 of 9)

- [Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis](https://mlsanity.com/paper/2603.06507/) (2026)
- [SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song Generation](https://mlsanity.com/paper/2502.13128/) (2025)
- [Where to Pay Attention in Sparse Training for Feature Selection?](https://mlsanity.com/paper/2211.14627/) (2022)
- [Detecting music deepfakes is easy but actually hard](https://mlsanity.com/paper/2405.04181/) (2024)
- [CycleDRUMS: Automatic Drum Arrangement For Bass Lines Using CycleGAN](https://mlsanity.com/paper/2104.00353/) (2021)
- [From Real to Cloned Singer Identification](https://mlsanity.com/paper/2407.08647/) (2024)
- [Melody or Machine: Detecting Synthetic Music with Dual-Stream Contrastive Learning](https://mlsanity.com/paper/2512.00621/) (2025)
- [Benchmarking Sub-Genre Classification For Mainstage Dance Music](https://mlsanity.com/paper/2409.06690/) (2024)
- [Variable-Length Audio Fingerprinting](https://mlsanity.com/paper/2603.23947/) (2026)

## Abstract (arXiv)

We here summarize our experience running a challenge with open data for musical genre recognition. Those notes motivate the task and the challenge design, show some statistics about the submissions, and present the results.