Statistically Efficient Methods for Pitch and DOA Estimation

Abstract

Traditionally, direction-of-arrival (DOA) and pitch estimation of multichannel, periodic sources have been considered as two separate problems. Separate estimation may render the task of resolving sources with similar DOA or pitch impossible, and it may decrease the estimation accuracy. Therefore, it was recently considered to estimate the DOA and pitch jointly. In this paper, we propose two novel methods for DOA and pitch estimation. They both yield maximum-likelihood estimates in white Gaussian noise scenarios, where the SNR may be different across channels, as opposed to state-of-the-art methods. The first method is a joint estimator, whereas the latter use a cascaded approach, but with a much lower computational complexity. The simulation results confirm that the proposed methods outperform state-of-the-art methods in terms of estimation accuracy in both synthetic and real-life signal scenarios.Traditionally, direction-of-arrival (DOA) and pitch estimation of multichannel, periodic sources have been considered as two separate problems. Separate estimation may render the task of resolving sources with similar DOA or pitch impossible, and it may decrease the estimation accuracy. Therefore, it was recently considered to estimate the DOA and pitch jointly. In this paper, we propose two novel methods for DOA and pitch estimation. They both yield maximum-likelihood estimates in white Gaussian noise scenar- ios, where the SNR may be different across channels, as opposed to state-of-the-art methods. The first method is a joint estimator, whereas the latter use a cascaded approach, but with a much lower computational complexity. The simulation results confirm that the proposed methods outperform state-of-the-art methods in terms of estimation accuracy in both synthetic and real-life signal scenarios