Time Delay Estimation from Broadband Signals using Single Frequency Filtering

Abstract

Estimation of time delay from the received broadband signals like speech, collected at two or more spatially distributed microphones, has many applications, ranging from speaker localization, speaker separation, determining the number of speakers and tracking a moving speaker. Cross-correlation based methods like the cross-correlation of signals directly and generalized cross-correlation based methods (GCC, GCC-PHAT) have been used for several years to estimate the time delay. Performance of these methods degrades due to noise, multipath reflections, and reverberation in a practical environment, like a live room. The objective of this thesis is to address the issues of robustness and accuracy of the estimated time delay by generating multiple evidence from several cross-correlation sequences using single frequency filtering (SFF) method for decomposition of the received signals into components, one at each frequency. The accuracy of the estimated delay is improved by exploiting the amplitude information of the samples of the cross-correlation sequence around the peak corresponding to the integer delay. The estimated fractional part of the delay is shown to be not only accurate, but also robust against different types and levels of additive noise degradations. The accuracy of the estimation of the fractional part is further improved by using a trained artificial neural network model for regression. Simulation experiments with known time delay are used to evaluate the accuracy and robustness of the proposed time delay estimation methods. Complementary information to the SFF magnitude is provided by the phase component of the SFF output. The time delays are estimated accurately and independently by using the signals reconstructed from the SFF phase alone. The complex SFF spectra at each instant are used to obtain the instantaneous time delay. The instantaneous time delay is exploited for separation of individual speakers from the mixed signals received at two spatially separated microphones in a live room.