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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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Features for Evaluating Source Localization Effectiveness in Sound Maps from Acoustic Cameras.

Luca Fredianelli¹, Gregorio Pedrini², Matteo Bolognese³

¹National Research Council (CNR), a Moruzzi 1, 56124 Pisa, Italy.

Sensors (Basel, Switzerland)

|July 27, 2024

Summary

This study introduces a method to analyze acoustic camera (AC) sound maps, extracting quantifiable data like source position and uncertainty. This advances AC technology for reliable noise emission analysis in various applications.

Keywords:

acoustic camera beamforming algorithms environmental noise microphone array noise measurements sound maps sound signals source localization

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Area of Science:

Acoustics
Signal Processing
Environmental Monitoring

Background:

Acoustic cameras (ACs) are increasingly used for noise source identification in environmental acoustics.
Current AC results lack certifiability due to variability in beamforming algorithms and hardware under different conditions.
Insufficient studies address the clarification of these measurement condition issues.

Purpose of the Study:

To develop a methodology for extracting analytical, quantitative features from AC-generated sound maps.
To enable objective comparison of data from different ACs and beamforming algorithms.
To provide a basis for future comparative studies in real-world acoustic scenarios.

Main Methods:

A measurement campaign was conducted using an AC with a known sound source in free-field conditions.
A methodology was developed to extract the coordinates of the maximum sound emission point from the visual sound map.
The distance between the identified emission point and the actual source position, along with associated uncertainty, was calculated.

Main Results:

The proposed method successfully gathered coordinates of the maximum emission point from sound maps.
The distance from the real source position and the uncertainty of this measurement were quantified.
The extracted analytical features provide a basis for comparing AC performance across different setups.

Conclusions:

The developed methodology allows for the extraction of objective, analytical data from AC sound maps.
This method facilitates standardized comparisons, enhancing the reliability and applicability of AC technology.
The approach is adaptable for analyzing intensity maps in fields beyond acoustics.