Features for Evaluating Source Localization Effectiveness in Sound Maps from Acoustic Cameras.
Luca FredianelliGregorio PedriniMatteo BologneseMarco BernardiniFrancesco FidecaroGaetano LicitraPublished in: Sensors (Basel, Switzerland) (2024)
Acoustic cameras (ACs) have become very popular in the last decade as an increasing number of applications in environmental acoustics are observed, which are mainly used to display the points of greatest noise emission of one or more sound sources. The results obtained are not yet certifiable because the beamforming algorithms or hardware behave differently under different measurement conditions, but at present, not enough studies have been dedicated to clarify the issues. The present study aims to provide a methodology to extract analytical features from sound maps obtained with ACs, which are generally only visual information. Based on the inputs obtained through a specific measurement campaign carried out with an AC and a known sound source in free field conditions, the present work elaborated a methodology for gathering the coordinates of the maximum emission point on screen, its distance from the real position of the source and the uncertainty associated with this position. The results obtained with the proposed method can be compared, thus acting as a basis for future comparison studies among calculations made with different beamforming algorithms or data gathered with different ACs in all real case scenarios. The method can be applicable to any other sector interested in gathering data from intensity maps not related to sound.
Keyphrases
- acute coronary syndrome
- machine learning
- randomized controlled trial
- electronic health record
- big data
- deep learning
- oxidative stress
- climate change
- high throughput
- air pollution
- molecular dynamics
- case control
- high intensity
- drinking water
- risk assessment
- human health
- mass spectrometry
- life cycle
- clinical evaluation