SALT: STANDARDIZED AUDIO EVENT LABEL TAXONOMY - Equipe Signal, Statistique et Apprentissage
Conference Papers Year : 2024

SALT: STANDARDIZED AUDIO EVENT LABEL TAXONOMY

Abstract

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.

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Dates and versions

hal-04695595 , version 1 (12-09-2024)

Identifiers

  • HAL Id : hal-04695595 , version 1

Cite

Paraskevas Stamatiadis, Michel Olvera, Slim Essid. SALT: STANDARDIZED AUDIO EVENT LABEL TAXONOMY. DCASE, Oct 2024, Tokyo, Japan. ⟨hal-04695595⟩
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