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A two-stage noise source identification technique based on a farfield random parametric array.

Mingsian R Bai¹, You Siang Chen¹, Yi-Yang Lo¹

¹Department of Power Mechanical Engineering, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu 30013, Taiwan.

The Journal of the Acoustical Society of America

|June 11, 2017

Summary

This summary is machine-generated.

This study introduces a novel two-stage algorithm for noise source identification using a farfield random array. The method accurately localizes and separates acoustic sources, enabling calculation of sound intensity and power.

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Area of Science:

Acoustics
Signal Processing
Computational Physics

Background:

Farfield arrays traditionally offer limited spatial resolution for noise source identification.
Accurate localization and separation of acoustic sources are crucial for effective noise control and source characterization.
Existing methods often struggle to quantify acoustic variables beyond relative source distribution.

Purpose of the Study:

To develop and validate a robust two-stage algorithm for noise source identification using a farfield random array.
To enhance the capability of farfield arrays for precise acoustic source localization and amplitude estimation.
To enable the calculation of absolute acoustic variables like sound pressure, particle velocity, sound intensity, and sound power.

Main Methods:

Implementation of a farfield random array with optimized microphone positions using simulated annealing.
A two-stage algorithm based on the equivalent source method (ESM) for source localization and separation.
Localization stage employs delay-and-sum and stochastic maximum likelihood algorithms; separation utilizes Tikhonov regularization or compressive sensing.

Main Results:

Successful localization and separation of noise sources were demonstrated through numerical simulations and experimental tests.
The proposed method allows for the quantitative calculation of acoustic variables, overcoming limitations of traditional farfield arrays.
Validation through objective and subjective tests confirms the efficacy of the developed algorithm.

Conclusions:

The devised two-stage algorithm effectively identifies and separates noise sources from farfield measurements.
This approach significantly advances the capabilities of acoustic arrays, enabling quantitative analysis of sound fields.
The method provides a powerful tool for noise source identification and acoustic energy quantification in various applications.