# Multi-label Open-set Audio Classification

arXiv: https://arxiv.org/abs/2310.13759 · HTML: https://mlsanity.com/paper/2310.13759/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2023.

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

- [Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds](https://mlsanity.com/paper/2403.09598/) (2024)

## Abstract (arXiv)

Current audio classification models have small class vocabularies relative to the large number of sound event classes of interest in the real world. Thus, they provide a limited view of the world that may miss important yet unexpected or unknown sound events. To address this issue, open-set audio classification techniques have been developed to detect sound events from unknown classes. Although these methods have been applied to a multi-class context in audio, such as sound scene classification, they have yet to be investigated for polyphonic audio in which sound events overlap, requiring the use of multi-label models. In this study, we establish the problem of multi-label open-set audio classification by creating a dataset with varying unknown class distributions and evaluating baseline approaches built upon existing techniques.