Sound source localization in dynamic environments with multiple moving speakers presents significant challenges due to reverberation, noise, and unknown source counts. To address these issues, this paper proposes an integrated deep-learning framework combining spatial spectrum estimation with blind source detection. The method employs a causal convolution–recurrent network (SRP-DPCRN) to extract robust spatial features from multichannel audio signals under adverse acoustic conditions. Subsequently, an iterative attention-based detection network (IASDNet) automatically identifies active sources from the estimated spatial spectrum without requiring prior knowledge of source quantity. Evaluated on both simulated datasets and the real-recorded LOCATA benchmark, the proposed system demonstrates superior performance in multi-source tracking scenarios, achieving an average detection accuracy of 96% with mean angular error below 3.5 degrees. The results confirm that joint optimization of feature learning and source counting provides an effective solution for blind localization in practical applications, significantly outperforming conventional and deep-learning baselines.
Yu et al. (Mon,) studied this question.