DOA estimation is an important task in signal processing that involves determining the angle of arrival of signals in an array of sensors or antennas. AI techniques, such as machine learning and deep learning, can significantly enhance DOA estimation by providing more accurate, efficient, robust, and adaptable solutions. AI algorithms can learn complex patterns and relationships in data, optimize for specific hardware, and adapt to changing signal environments. This work explores the significance of AI in DOA estimation and highlights the potential benefits that AI can bring to this critical task in signal processing. M-SVM and UCNN models are studied in this work. Combining these learning models provides a robust DOA estimation corresponding to the SH features. Performance measured in terms of accuracy, root mean square error, and complexity yield intriguing findings that encourage using the proposed model in real-world scenarios.

Recent advancements in artificial intelligence (AI) have shown potential solutions to acoustic source localization in three-dimensional space. This article proposes a new low-complex AI-based framework in the spherical harmonics (SH) domain for efficient direction of arrival (DOA) estimation. The SH coefficients are the key features for the DOA estimation and are obtained from the SH decomposition (SHD) of the spherical microphone array (SMA) recordings. Subsequently, the unified convolutional neural network (UCNN) model is trained to estimate the source azimuth and elevation from the phase and magnitude of the SH coefficient. Since the relation between the azimuth and elevation with phase and magnitude of the SH coefficient is subjective, a high volume of data are required to train the model. In this context, the symmetric properties of the SH basis function are explored to obtain the SH implicit symmetric coefficients (SH-ISCs) that split the 3-D space into octant classes. Within eac...