Wideband compressive beamforming tomography for drive-by large-scale acoustic source mapping.

Noise-mapping is an effective sound visualization tool for the identification of urban noise hotspots, which is crucial to taking targeted measures to tackle environmental noise pollution. This paper develops a high-resolution wideband acoustic source mapping methodology using a portable microphone array, where the joint localization and power spectrum estimation of individual sources sparsely distributed over a large region are achieved by tomographic imaging with the multi-frequency delay-and-sum beamforming power outputs from multiple array positions. Exploiting the fact that a wideband source has a common spatial signal-support across the frequency spectrum, two-dimensional tomographic maps are produced by applying compressive sensing techniques including group least absolute shrinkage selection operator formulation and sparse Bayesian learning to promote group sparsity over multiple frequency bands.

Large-region acoustic source mapping using a movable array and sparse covariance fitting.

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.

Drive-by large-region acoustic noise-source mapping via sparse beamforming tomography.

Environmental noise is a risk factor for human physical and mental health, demanding an efficient large-scale noise-monitoring scheme. The current technology, however, involves extensive sound pressure level (SPL) measurements at a dense grid of locations, making it impractical on a city-wide scale. This paper presents an alternative approach using a microphone array mounted on a moving vehicle to generate two-dimensional acoustic tomographic maps that yield the locations and SPLs of the noise-sources sparsely distributed in the neighborhood traveled by the vehicle.

Frequency-domain beamformers using conjugate gradient techniques for speech enhancement.

A multiple-iteration constrained conjugate gradient (MICCG) algorithm and a single-iteration constrained conjugate gradient (SICCG) algorithm are proposed to realize the widely used frequency-domain minimum-variance-distortionless-response (MVDR) beamformers and the resulting algorithms are applied to speech enhancement. The algorithms are derived based on the Lagrange method and the conjugate gradient techniques. The implementations of the algorithms avoid any form of explicit or implicit autocorrelation matrix inversion.