Joint DOA Estimation in Spherical Harmonics Domain using Low Complexity CNN

Abstract

Direction of arrival (DOA) estimation for multi-channel speech enhancement is a challenging problem. In this context, this paper proposes a new method for joint DOA estimation using a low complexity convolutional neural network (CNN) architecture. The spherical harmonic (SH) coefficients of the received speech signal are obtained from the spherical harmonics decomposition (SHD). The magnitude and phase features are extracted from these SH coefficients and combined as a single feature for training the CNN. A single CNN model is trained using these combined features in contrast to two CNN models used in earlier work. Both azimuth and elevation are then obtained for estimation of DOA from this single CNN. Extensive simulations are also conducted for the performance evaluation of the proposed low complexity CNN model. It is observed that the proposed CNN model provides robust DOA estimates at the various signal to noise ratios (SNR) and reverberation times with reduced computational complexity. Performance evaluated in terms of the gross error (GE) and run-time complexity also provides interesting results motivating the use of the proposed model in practical applications.