Sound event detection by intermittency ratio criterium and source classification by deep learning techniques
Year: 2025
Type: article
Abstract: Urban environments are characterized by a complex interplay of various sound sources, which significantly influence the overall soundscape quality. This study presents a methodology that combines the intermittency ratio (IR) metric for acoustic event detection with deep learning (DL) techniques for the classification of sound sources associated with these events. The aim is to provide an automated tool for detecting and categorizing polyphonic acoustic events, thereby enhancing our ability to assess and manage environmental noise. Using a dataset collected in the city center of Barcelona, our results demonstrate the effectiveness of the IR metric in successfully detecting events from diverse categories. Specifically, the IR captures the temporal variations of sound pressure levels due to significant noise events, enabling their detection but not providing information on the associated sound sources. To fill this weakness, the DL-based classification system, which uses a MobileNet convolutional neural network, shows promise in identifying foreground sound sources. Our findings highlight the potential of DL techniques to automate the classification of sound sources, providing valuable insights into the acoustic environment. The proposed methodology of combining the two above techniques represents a step forward in automating acoustic event detection and classification in urban soundscapes and providing important information to manage noise mitigation actions.
Source: Noise Mapping
Authors: Ester Vidaña-Vila, Giovanni Brambilla, Rosa Ma Alsina-Pagès
Institutions: Universitat Ramon Llull, University of Milano-Bicocca
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,187
Funder: Generalitat de Catalunya
OpenAlex ID: W4409525141