# SALT: Standardized Audio event Label Taxonomy

arXiv: https://arxiv.org/abs/2409.11746 · HTML: https://mlsanity.com/paper/2409.11746/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2024.

## Built on by (most cited first, 3 of 3)

- [From Large-scale Audio Tagging to Real-Time Explainable Emergency Vehicle Sirens Detection](https://mlsanity.com/paper/2506.23437/) (2025)
- [IS3 : Generic Impulsive--Stationary Sound Separation in Acoustic Scenes using Deep Filtering](https://mlsanity.com/paper/2509.02622/) (2025)
- [SonicCaps: Large-Scale Diverse and Fine-Grained Captioning for Improved Audio-Retrieval](https://mlsanity.com/paper/2609.02343/) (2026)

## Abstract (arXiv)

Machine listening systems often rely on fixed taxonomies to organize and label audio data, key for training and evaluating deep neural networks (DNNs) and other supervised algorithms. However, such taxonomies face significant constraints: they are composed of application-dependent predefined categories, which hinders the integration of new or varied sounds, and exhibits limited cross-dataset compatibility due to inconsistent labeling standards. To overcome these limitations, we introduce SALT: Standardized Audio event Label Taxonomy. Building upon the hierarchical structure of AudioSet's ontology, our taxonomy extends and standardizes labels across 24 publicly available environmental sound datasets, allowing the mapping of class labels from diverse datasets to a unified system. Our proposal comes with a new Python package designed for navigating and utilizing this taxonomy, easing cross-dataset label searching and hierarchical exploration. Notably, our package allows effortless data aggregation from diverse sources, hence easy experimentation with combined datasets.