High-Resolution Identification of Sound Sources Based on Sparse Bayesian Learning with Grid Adaptive Split Refinement
Abstract
:1. Introduction
2. Problem Statement
3. Inverse Solving Process
3.1. Sparse Bayesian Learning Solver
3.2. Signal Separation Based on Robust Principal Component Analysis
3.3. Sound Source Identification Method Based on Grid Splitting and Refinement
Algorithm 1 VG-SBL Algorithm. |
Input: The snapshot sound pressure: . 1: Discretize the source plane into a grid surface with as the aperture and as the spacing. 2: Establish the transfer matrix between the sound pressure and the source surface. 3: The initial value of the iteration number “” is set to 0. The decay factor and the iteration stopping threshold are set. 4: while . 5: The initial source strength is obtained by sparse Bayesian learning. 6: Split the meshes to update near the local maximum of , with and . 7: Remove duplicate grid points and construct a new transfer model . 8: Form a new sound pressure , and enter it into the MoG-RPCA model. 9: Replace the new sound pressure with the separated low-rank matrix: . 10: . 11: end while Output: The estimation of source strength . |
4. Simulation
4.1. Simulation Experiment Setup
4.2. Discussion of Results
4.2.1. Parameter Discussion
4.2.2. Method Performance Discussion
- (1)
- Influence of source frequency
- (2)
- Influence of SNR
- (3)
- The Impact of Microphone Arrays
4.2.3. Image Results of Different Methods
4.2.4. Calculation Time Comparison
5. Experimental Validation
5.1. Experimental Design
5.2. Experimental Results
5.2.1. Signal Time–Frequency Analysis
5.2.2. Sound Source Imaging Cloud Maps
5.2.3. Results
- (1)
- Dynamic range
- (2)
- Location distribution of sound sources
- (3)
- Amplitude error in identification of sound sources
- (4)
- RMSEL and RMSEM
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Method | Average RMSEL (m) | Average RMSEM (dB) |
---|---|---|
SBL | 0.16 | 4.82 |
OMP | 0.32 | 5.42 |
FISTA | 0.20 | 27.11 |
Off-grid-GL1 | 0.20 | 6.94 |
RPCA-SBL | 0.18 | 4.38 |
VG-SBL | 0.09 | 2.45 |
Method | Average RMSEL (m) | Average RMSEM (dB) |
---|---|---|
SBL | 0.09 | 3.67 |
OMP | 0.19 | 3.86 |
FISTA | 0.17 | 23.99 |
Off-grid-GL1 | 0.11 | 6.04 |
RPCA-SBL | 0.09 | 3.24 |
VG-SBL | 0.05 | 1.31 |
Method | Average RMSEL (m) | Average RMSEM (dB) |
---|---|---|
SBL | 0.54 | 4.99 |
OMP | 0.58 | 7.64 |
FISTA | 0.49 | 33.99 |
Off-grid-GL1 | 0.53 | 5.66 |
RPCA-SBL | 0.55 | 4.62 |
VG-SBL | 0.25 | 3.23 |
Source | Sound Pressure Level (dB) |
---|---|
Source A | 88.8 |
Source B | 92.3 |
Source C | 90.5 |
Method | Time (s) |
---|---|
SBL | 1.51 |
OMP | 0.04 |
FISTA | 16.84 |
Off-grid-GL1 | 28.88 |
RPCA-SBL | 1.71 |
VG-SBL | 7.80 |
Frequency (Hz) | 300 | 500 |
---|---|---|
Sound pressure level of Source A (dB) | 106.7 | 107.0 |
Sound pressure level of Source B (dB) | 106.7 | 107.0 |
Sound pressure level of Source C (dB) | 86.7 | 87.0 |
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Pan, W.; Feng, D.; Shi, Y.; Chen, Y.; Li, M. High-Resolution Identification of Sound Sources Based on Sparse Bayesian Learning with Grid Adaptive Split Refinement. Appl. Sci. 2024, 14, 7374. https://doi.org/10.3390/app14167374
Pan W, Feng D, Shi Y, Chen Y, Li M. High-Resolution Identification of Sound Sources Based on Sparse Bayesian Learning with Grid Adaptive Split Refinement. Applied Sciences. 2024; 14(16):7374. https://doi.org/10.3390/app14167374
Chicago/Turabian StylePan, Wei, Daofang Feng, Youtai Shi, Yan Chen, and Min Li. 2024. "High-Resolution Identification of Sound Sources Based on Sparse Bayesian Learning with Grid Adaptive Split Refinement" Applied Sciences 14, no. 16: 7374. https://doi.org/10.3390/app14167374