Sound Source Localization Algorithm of Microphone Array Based on Incremental Broad Learning System

Sound source localization is a technique that utilizes microphone arrays to detect the position of sound sources. It has a wide range of applications in areas such as smart homes, robot navigation, and conference recording. However, due to the complexity of the acoustic environment and the impact of noise interference, the accuracy of localization algorithms has always been a core concern in this field. Traditional time difference of arrival (TDOA) techniques struggle to achieve high precision and efficiency in localization results. To address this issue, this paper proposes a microphone array sound source localization method based on incremental broad learning (Enhance) algorithm. This method extracts shallow and deep features from audio signals and maps them to feature nodes and enhancement nodes in the broad learning system (BLS); a neural network model is constructed, which allows for fast adjustment of network structure and parameters. The model employs ridge regression to calculate connection weights and utilizes enhancement nodes to modify and optimize the feature nodes, thus achieving accurate prediction of sound source locations. The proposed method is experimentally validated using the NOIZEUS dataset and compared with the single-structure broad learning (One-shot) algorithm, back-propagation neural network (BP) algorithm, and recurrent neural network (RNN) algorithm. In the experiments, a microphone array consisting of four microphones is used, with a room size of 5 m x 4 m x 3 m. Different reverberation times (T60) and signal-to-noise ratios (SNRs) are employed to simulate various acoustic environments, and the performance of the four algorithms is evaluated in terms of outlier percentage and mean-squared error (MSE). The results demonstrate that under high reverberation (T60 = 700 ms) and low SNR (SNR = 0 dB) conditions, the proposed method achieves outlier percentage of only 0.308% and MSE of 0.92 degrees. Compared to the other three algorithms, Enhance algorithm exhibits superior localization accuracy, noise robustness, and stability, thus holding significant research and practical value.