MAVD: A Dataset for Sound Event Detection in Urban Environments

Abstract

We describe the public release of a dataset for sound event detection in urban environments, namely MAVD, which is the first of a series of datasets planned within an ongoing research project for urban noise monitoring in Montevideo city, Uruguay. This release focuses on traffic noise, MAVD-traffic, as it is usually the predominant noise source in urban environments. An ontology for traffic sounds is proposed, which is the combination of a set of two taxonomies: vehicle types (e.g. car, bus) and vehicle components (e.g. engine, brakes), and a set of actions related to them (e.g. idling, accelerating). Thus, the proposed ontology allows for a flexible and detailed description of traffic sounds. We also provide a baseline of the performance of state-of-the-art sound event detection systems applied to the dataset.