This paper describes a source tracking technique in a reverberant environment using a new combination of an adaptive species-based particle swarm optimization (ASPSO) algorithm and a multisignal classification (MUSIC) algorithm. To mitigate the effects of reverberation, an insightful dereverberation method based on an online autoregressive (AR) array and a minimum variance distortionless response (MVDR) beamformer is developed to dereverberate the microphone signal prior to direction of arrival (DOA) estimation using MUSIC. On the basis of several evolutionary schemes, ASPSO enables rapid tracking by finding local maxima in the MUSIC pseudospectrum. In the ASPSO algorithm, particles are divided into different species, where each species is associated with a sound source. As the sound source moves, the DOA information is dynamically updated using ASPSO, in which the inertia weight decreases progressively to prevent premature convergence. Two update rules for adapting the filter coefficients are employed for drastically moving sources. Simulations and experiments are conducted using a circular microphone array to validate the proposed ASPSO with AR (ASPSO-AR) algorithm. The results demonstrate that ASPSO-AR requires one-third of the processing time of the grid search (GS) method. In addition, the root-mean-square error (RMSE) of the ASPSO-AR algorithm is 10° less than that of the GS method.

This paper presents a computationally efficient algorithm for multiple source localization and signal extraction (SLSE). Posed as an underdetermined system, a novel compressed sensing (CS) algorithm is proposed to address SLSE problems in one stage. A Least Absolute and Selection Operator (LASSO) problem is first formulated and solved jointly for the source locations and signal amplitudes. A computationally efficient and noise-resilient algorithm is developed on the basis of the complex Proximal Gradient (Proxgrad) method. It follows that the nonzero entries of the optimal solutions give rise to the amplitudes and directions of sound sources. To further enhance the separation quality, soft thresholds based on W-disjoint orthogonality is exploited. Experiments are conducted to compare the proposed SLSE method with several baselines in terms of localization and separation metrics. The results showed that the proposed LASSO-Proxgrad algorithm yielded superior localization and signal extraction performance with the minimal processing time compared to the baselines.