Gain and phase calibration of sensor arrays from ambient noise by cross-spectral measurements fitting.

We address the problem of blind gain and phase calibration of a sensor array from ambient noise. The key motivation is to ease the calibration process by avoiding a complex procedure setup. We show that computing the sample covariance matrix in a diffuse field is sufficient to recover the complex gains.

Sequential sensor selection for the localization of acoustic sources by sparse Bayesian learning.

This paper deals with the design of sensor arrays in the context involving the localization of a few acoustic sources. Sparse approximation is known to be effective to find the source locations, but it depends on different array characteristics, such as the number of sensors and the array geometry. The present paper tackles this array design problem under the form of a sequential sensor selection procedure.

Mapping uncertainties involved in sound source reconstruction with a cross-spectral-matrix-based Gibbs sampler.

The reconstruction of sound sources by using inverse methods is known to be prone to estimation errors due to measurement noise, model mismatch, and poor conditioning of the inverse problem. This paper introduces a solution to map the estimation errors together with the reconstructed sound sources. From a Bayesian perspective, it initializes a Gibbs sampler with the Bayesian focusing method.

Geometric calibration of very large microphone arrays in mismatched free field.

Large microphone arrays are an efficient means for source localization thanks to a wide aperture and a great number of sensors. When such arrays are deployed in situ, accurate geometric calibration becomes essential to obtain the microphone positions. In free field, the classic procedures rely on measured Times of Arrival (TOA) or Time Differences of Arrival (TDOA) between the microphones and several controlled sources.

A robust and passive method for geometric calibration of large arrays.

This paper presents a complete strategy for the geometry estimation of large microphone arrays of arbitrary shape. Largeness is intended here in both number of microphones (hundreds) and size (few meters). Such arrays can be used for various applications in open or confined spaces like acoustical imaging, source identification, or speech processing.