DOA Estimation using Multiclass-SVM in Spherical Harmonics Domain

Direction of arrival (DOA) estimation is still a challenging and fundamental problem in acoustic signal processing. This paper proposes a new method for DOA estimation that utilizes the support vector machine (SVM) based classification. The source signal is recorded by the spherical microphone array (SMA) and decomposed into the spherical harmonics domain. The phase and the magnitude features are calculated from the spherical harmonics (SH) decomposed signals. A multiclass support vector machine (M-SVM) algorithm is implemented to classify these phase and magnitude features to the DOA classes. Since the SVM is a non-probabilistic and deterministic model, it is computationally faster and highly reduced complexity than the neural network-based learning models. Extensive simulations are conducted for the performance evaluation of the proposed method. It is observed that the proposed model provides robust DOA estimates at various signal-to-noise ratios (SNR) and reverberation time. Performance evaluated in terms of the root mean square error (RMSE) provides interesting results motivating the use of the proposed model in practical applications.