| Abstract | This paper introduces a novel two-step algorithm for microphone array fusion to enhance Sound Source Localization (SSL) in indoor reverberant environments. The proposed method intelligently selects Angle of Arrival (AoA) estimates to reduce localization errors while maintaining computational efficiency. Through simulation analysis using both simulated and real Room Impulse Responses (RIRs), we identify that AoA accuracy varies depending on the sound source location, leading to unreliable estimates from certain microphone arrays. To address this, we propose a method to exclude these unreliable AoAs from SSL, improving overall localization performance.To further evaluate the effectiveness of the proposed approach, we compare it to a deep learning-based SSL method, where a Deep Neural Network (DNN) predicts source locations based on estimated AoAs. First, we compare our method to an approach that uses all available AoAs without selection, demonstrating that the two-step algorithm reduces Mean Absolute Error (MAE) by up to 50%. Next, we compare our method with the DNN-based approach, which achieves a 6.6% lower MAE while having higher 25th and 75th percentile values, and is computationally more complex and requires extensive training data. These results highlight the ability of the two-step method to efficiently determine which AoAs to use in order to maintain more accurate SSL. These findings emphasize the practical value of the proposed method in improving SSL accuracy in challenging acoustic conditions. |
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