  We propose an advance Steered Response Power (SRP) method for localizing
multiple sources. While conventional SRP performs well in adverse conditions,
it remains to struggle in scenarios with closely neighboring sources, resulting
in ambiguous SRP maps. We address this issue by applying sparsity optimization
in SRP to obtain high-resolution maps. Our approach represents SRP maps as
multidimensional matrices to preserve time-frequency information and further
improve performance in unfavorable conditions. We use multi-dictionary Sparse
Bayesian Learning to localize sources without needing prior knowledge of their
quantity. We validate our method through practical experiments with a
16-channel planar microphone array and compare against three other SRP and
sparsity-based methods. Our multidimensional SRP approach outperforms
conventional SRP and the current state-of-the-art sparse SRP methods for
localizing closely spaced sources in a reverberant room.
