The Steered Response Power with PHAT transform (SRP-PHAT) or Global Coherence Field (GCF), has become a standard method for acoustic source localization, thanks to their simplicity, computational inexpensiveness and robustness against mid-high reverberation. However, originally formulated for the single source localization case, it does not apply satisfactorily to the multiple source case. In this paper, we analyze the structure of the spatial function and reshape it according to a generic multidimensional metric. We show that traditional functions are based on the L1 norm which is prone to generate ambiguous locations with high likelihood (i.e. ghosts). A more generic multidimensional kernel based on higher norms and on a partitioned representation of the cross-power spectrum is introduced, which better exploits the source sparseness in the discrete time-frequency domain. Evaluation results over simulated data show that the new spatial functions considerably improve the detection of multiple competing sources in both spatial and multidimensional TDOA domains.

Enhanced multidimensional spatial functions for unambiguous localization of multiple sparse acoustic sources

Nesta, Francesco;Omologo, Maurizio
2012

Abstract

The Steered Response Power with PHAT transform (SRP-PHAT) or Global Coherence Field (GCF), has become a standard method for acoustic source localization, thanks to their simplicity, computational inexpensiveness and robustness against mid-high reverberation. However, originally formulated for the single source localization case, it does not apply satisfactorily to the multiple source case. In this paper, we analyze the structure of the spatial function and reshape it according to a generic multidimensional metric. We show that traditional functions are based on the L1 norm which is prone to generate ambiguous locations with high likelihood (i.e. ghosts). A more generic multidimensional kernel based on higher norms and on a partitioned representation of the cross-power spectrum is introduced, which better exploits the source sparseness in the discrete time-frequency domain. Evaluation results over simulated data show that the new spatial functions considerably improve the detection of multiple competing sources in both spatial and multidimensional TDOA domains.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/103804
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