Enzo De Sena

For acoustic source localization, a map of the acoustic scene as obtained by
the steered response power (SRP) approach can be employed. In SRP, the
frequency-weighted output power of a beamformer steered towards a set of
candidate locations is obtained from generalized cross-correlations (GCCs). Due
to the dense grid of candidate locations, conventional SRP exhibits a high
computational complexity. While a number of low-complexity SRP-based
localization approaches using non-exhaustive spatial search have been proposed,
few studies aim to construct a full SRP map at reduced computational cost. In
this paper, we propose two scalable approaches to this problem. Expressing the
SRP map as a matrix transform of frequency-domain GCCs, we decompose the SRP
matrix into a sampling matrix and an interpolation matrix. While the sampling
operation can be implemented efficiently by the inverse fast Fourier transform
(iFFT), we propose to use optimal low-rank or sparse approximations of the
interpolation matrix for further complexity reduction. The proposed approaches,
refered to as sampling + low-rank interpolation-based SRP (SLRI-SRP) and
sampling + sparse interpolation-based SRP (SSPI-SRP), are evaluated in a
near-field (NF) and a far-field (FF) localization scenario and compared to a
state-of-the-art low-rank-based SRP approach (LR-SRP). The results indicate
that SSPI-SRP outperforms both SLRI-SRP and LR-SRP over a wide complexity range
in terms of approximation error and localization accuracy, achieving a
complexity reduction of two to three orders of magnitude as compared to
conventional SRP. A MATLAB implementation is available online.