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Large-region acoustic source mapping using a movable array and sparse covariance fitting.
- Source :
- Journal of the Acoustical Society of America; Jan2017, Vol. 141 Issue 1, p357-372, 16p, 1 Color Photograph, 2 Charts, 11 Graphs
- Publication Year :
- 2017
-
Abstract
- Large-region acoustic source mapping is important for city-scale noise monitoring. Approaches using a single-position measurement scheme to scan large regions using small arrays cannot provide clean acoustic source maps, while deploying large arrays spanning the entire region of interest is prohibitively expensive. A multiple-position measurement scheme is applied to scan large regions at multiple spatial positions using a movable array of small size. Based on the multiple-position measurement scheme, a sparse-constrained multiple-position vectorized covariance matrix fitting approach is presented. In the proposed approach, the overall sample covariance matrix of the incoherent virtual array is first estimated using the multiple-position array data and then vectorized using the Khatri-Rao (KR) product. A linear model is then constructed for fitting the vectorized covariance matrix and a sparse-constrained reconstruction algorithm is proposed for recovering source powers from the model. The user parameter settings are discussed. The proposed approach is tested on a 30 m × 40 m region and a 60 m × 40 m region using simulated and measured data. Much cleaner acoustic source maps and lower sound pressure level errors are obtained compared to the beamforming approaches and the previous sparse approach [Zhao, Tuna, Nguyen, and Jones, Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) (2016)]. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00014966
- Volume :
- 141
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Journal of the Acoustical Society of America
- Publication Type :
- Academic Journal
- Accession number :
- 121065632
- Full Text :
- https://doi.org/10.1121/1.4974054