Joint Acoustic Localization and Dereverberation Through Plane Wave Decomposition and Sparse Regularization

Abstract

Acoustic source localization and dereverberation are formulated jointly as an inverse problem. The inverse problem consists of the approximation of the sound field measured by a set of microphones. The recorded sound pressure is matched with that of a particular acoustic model based on a collection of plane waves arriving from different directions at the microphone positions. In order to achieve meaningful results, spatial and spatio-spectral sparsity can be promoted in the weight signals controlling the plane waves. The large-scale optimization problem resulting from the inverse problem formulation is solved using a first order optimization algorithm combined with a weighted overlap-add procedure. It is shown that once the weight signals capable of effectively approximating the sound field are obtained, they can be readily used to localize a moving sound source in terms of direction of arrival (DOA) and to perform dereverberation in a highly reverberant environment. Results from simulation experiments and from real measurements show that the proposed algorithm is robust against both localized and diffuse noise exhibiting a noise reduction in the dereverberated signals.

Low frequency sound field reconstruction in a non-rectangular room using a small number of microphones

“…For a room with complex geometry, the complexity of the mode shape functions will also increase for higherorder room modes which will generally require a higher number of plane waves to converge. Even when using a least norm method to increase the number of plane waves, the compromise between regularization and instability of the mode shape approximation [30,31] means that the accuracy still relies heavily on the number of measurements available. Furthermore, as the frequency gets higher, the modal density will increase which means that the average distance (in Hz) between two consecutive modes will be smaller and induce more difficulties for the modal estimation framework.…”

Gridless DOA Estimation With Multiple Frequencies

“…The multi-frequency (or multi-dictionary) model [14], [15], [16], [17], [18] has shown success in modeling wideband signals.…”

“…A. Related Work 1) Multiple Frequencies: Multiple frequencies decompose a wideband signal into multiple narrowband signals and therefore are widely applied in acoustics source localization [15], [16], [18] when the signal contains a wide range of frequency bins and cannot be characterized by a narrowband model. Some grid-based sparse localization approaches for the multiple frequencies were proposed [14], [15], [17], [18], [33] for robustness and aliasing suppression.…”

“…Variants of CSSM, such as the weighted average of signal subspaces method [13] and the test of orthogonality of projected subspaces method [14] were also proposed. Recently, some wideband DOA estimation methods based on sparse recovery have also been developed [7]- [10], [15]- [19]. These sparsity-based methods have demonstrated superior performance compared to conventional methods and generally require much fewer samples.…”

Gridless DOA Estimation Under the Multi-Frequency Model

“…A. Related Work 1) Multiple Frequencies: Multiple frequencies decompose a wideband signal into multiple narrowband signals and therefore are widely applied in acoustics source localization [7], [8], [10] when the signal contains a wide range of frequency bins and cannot be characterized by a narrowband model. Some grid-based sparse localization approaches for the multiple frequencies were proposed [6], [7], [9], [10], [24] for robustness and aliasing suppression.…”