NoisenseDB: An Urban Sound Event Database to Develop Neural Classification Systems for Noise-Monitoring Applications

Abstract

The use of continuous monitoring systems to control aspects such as noise pollution has grown in recent years. The commercial monitoring systems used to date only provide information on noise levels but do not identify the noise sources that generate them. The identification of noise sources is an important aspect in order to apply corrective measures to mitigate the noise levels. In this sense, new technological advances like machine listening can enable the addition of other capabilities to sound monitoring systems such as the detection and classification of noise sources. Despite the increasing development of these systems, researchers have to face some shortcomings. The most frequent ones are on the one hand, the lack of data recorded in real environments and on the other hand, the need for automatic labelling of large volumes of data collected by working monitoring systems. In order to address these needs, in this paper, we present our own sound database recorded in an urban environment. Some baseline results for the database are provided using two original convolutional neural network based sound events classification systems. Additionally, a state of the art transformer-based audio classification system (AST) has been applied to obtain some baseline results. Furthermore, the database has been used for evaluating a semi-supervised strategy to train a classifier for automatic labelling that can be refined by human labellers afterwards.