A Strongly-Labelled Polyphonic Dataset of Urban Sounds with Spatiotemporal Context

TL;DR

This paper presents SINGA:PURA, a comprehensive, strongly labelled urban sound dataset with spatiotemporal data from Singapore, designed for sound event detection, classification, and localization, including a hierarchical label taxonomy.

Contribution

Introduction of SINGA:PURA, a new urban sound dataset with detailed annotations and a hierarchical taxonomy, tailored for diverse sound analysis tasks in an Asian urban context.

Findings

Dataset includes diverse urban sound events with spatiotemporal annotations

Baseline model performance provided as a benchmark for future research

Hierarchical label taxonomy enhances compatibility with existing datasets

Abstract

This paper introduces SINGA:PURA, a strongly labelled polyphonic urban sound dataset with spatiotemporal context. The data were collected via several recording units deployed across Singapore as a part of a wireless acoustic sensor network. These recordings were made as part of a project to identify and mitigate noise sources in Singapore, but also possess a wider applicability to sound event detection, classification, and localization. This paper introduces an accompanying hierarchical label taxonomy, which has been designed to be compatible with other existing datasets for urban sound tagging while also able to capture sound events unique to the Singaporean context. This paper details the data collection, annotation, and processing methodologies for the creation of the dataset. We further perform exploratory data analysis and include the performance of a baseline model on the dataset as a benchmark.