Octant Spherical Harmonics Features for Source Localization Using Artificial Intelligence Based on Unified Learning Framework

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 each octant, the phase and magnitude of the SH coefficients exhibit one-to-one correspondence with the source azimuth and elevation and execute the data redundancy. This work can be divided into two parts, a multiclass support vector machine (M-SVM) is investigated to obtain the octant classes from the SH-ISC in the first part. In the second part, the UCNN model is developed to estimate the DOA angles in each octant class. Further, the proposed technique is computationally efficient compared to the baseline learning algorithms in terms of computational and run-time complexity.