# Open Set Recognition For Music Genre Classification

arXiv: https://arxiv.org/abs/2209.07548 · HTML: https://mlsanity.com/paper/2209.07548/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2022.

## Built on by (most cited first, 1 of 1)

- [Joint Music and Language Attention Models for Zero-shot Music Tagging](https://mlsanity.com/paper/2310.10159/) (2023)

## Abstract (arXiv)

We explore segmentation of known and unknown genre classes using the open source GTZAN and FMA datasets. For each, we begin with best-case closed set genre classification, then we apply open set recognition methods. We offer an algorithm for the music genre classification task using OSR. We demonstrate the ability to retrieve known genres and as well identification of aural patterns for novel genres (not appearing in a training set). We conduct four experiments, each containing a different set of known and unknown classes, using the GTZAN and the FMA datasets to establish a baseline capacity for novel genre detection. We employ grid search on both OpenMax and softmax to determine the optimal total classification accuracy for each experimental setup, and illustrate interaction between genre labelling and open set recognition accuracy.