Robust online direction of arrival estimation using low dimensional spherical harmonic features

Abstract

Signal processing in spherical harmonic domain has the ability to decouple frequency dependent and location dependent components of the signal received. A method for low dimensional spherical harmonic feature extraction is proposed in this work for DOA estimation in noisy and reverberant environments. The features are extracted using frequency smoothing and a transformation which makes them frequency and signal invariant. Additionally an online manifold regularization framework is explored which utilizes the proposed spherical harmonic features to compute real time DOA estimates. This framework minimizes an instantaneous risk function and finds an inverse mapping function that maps spherical harmonic features to the DOA estimate. Performance of the proposed DOA estimation method is then compared with DOA estimates obtained from features such as generalized cross correlation and relative transfer function in a semi-supervised manifold regularization framework. Experimental results on DOA estimation in terms of root mean square error and probability of resolution indicate a reasonable improvement in the localization performance along with significant reduction in feature dimension.