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Blind Calibration of Environmental Acoustics Measurements Using Smartphones.

Ayoub Boumchich¹, Judicaël Picaut¹, Pierre Aumond¹

¹UMRAE, CEREMA, Univ Gustave Eiffel, F-44344 Bouguenais, France.

Sensors (Basel, Switzerland)

|February 24, 2024

Summary

Environmental noise mapping faces challenges with traditional methods. This study adapts a blind calibration technique for smartphone acoustic data, improving noise measurement accuracy and reliability for better environmental noise control.

Keywords:

calibration environmental noise noise mapping smartphone application

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Area of Science:

Environmental Science
Acoustics
Geoinformatics

Background:

Environmental noise pollution is a significant public health and social concern.
Current noise mapping relies on numerical simulations or sparse acoustic sensors, limiting accuracy and spatial coverage.
Citizen science using smartphones offers potential for broader acoustic data collection but suffers from calibration biases.

Purpose of the Study:

To adapt and evaluate a blind calibration method for smartphone-based acoustic measurements.
To address the challenge of smartphone sensor bias in environmental noise monitoring.
To improve the reliability and representativeness of noise maps generated from citizen science data.

Main Methods:

Adaptation of a "blind calibration" technique, which relies on simultaneous measurements from multiple sensors in the same location.
Application of the adapted method to datasets from the NoiseCapture smartphone application.
Testing the method's performance using existing calibration data for select smartphones.

Main Results:

The adapted blind calibration method shows potential for correcting biases in smartphone acoustic measurements.
Analysis of sensor data crossings allows for the calibration of a large set of distributed sensors.
The study validates the approach on real-world NoiseCapture data.

Conclusions:

Blind calibration is a viable strategy to enhance the accuracy of smartphone-based environmental noise monitoring.
This approach can improve the quality of noise maps and inform effective environmental noise control policies.
Citizen science, when properly calibrated, can be a powerful tool for large-scale acoustic data acquisition.