Abstract
Two adaptive algorithms are presented for robust time delay estimation (TDE) in acoustic environments with a large amount of background noise and reverberation. Recently, an adaptive eigenvalue decomposition (EVD) algorithm has been developed for TDE in highly reverberant acoustic environments. In this paper, we extend the adaptive EVD algorithm to noisy and reverberant acoustic environments, by deriving an adaptive stochastic gradient algorithm for the generalized eigenvalue decomposition (GEVD) or by prewhitening the noisy microphone signals. We have performed simulations using a localized and a diffuse noise source for several SNRs, showing that the time delays can be estimated more accurately using the adaptive GEVD algorithm than using the adaptive EVD algorithm. In addition, we have analyzed the sensitivity of the adaptive GEVD algorithm with respect to the accuracy of the noise correlation matrix estimate, showing that its performance may be quite sensitive, especially for low SNR scenarios.
Highlights
In many speech communication applications, such as teleconferencing, hand-free voice-controlled systems, and hearing aids, it is desirable to localize the dominant speaker
Room reverberation is considered to be the main problem for time delay estimation (TDE) [7], but acoustic background noise can considerably decrease the performance of TDE algorithms
We have presented two adaptive algorithms for robust TDE in adverse acoustic environments where a large amount of reverberation and additive noise is present
Summary
In many speech communication applications, such as teleconferencing, hand-free voice-controlled systems, and hearing aids, it is desirable to localize the dominant speaker. We extend the adaptive EVD algorithm for TDE to the spatiotemporally colored noise case by using an adaptive generalized eigenvalue decomposition (GEVD) algorithm or by prewhitening the noisy microphone signals. It is shown that if the length of the impulse responses is either known or can be overestimated, the complete impulse responses can be identified from the EVD of the speech correlation matrix (noiseless case and spatiotemporally white noise case) or from the GEVD of the speech and the noise correlation matrices (colored noise case) These batch impulse response estimation procedures form the basis for deriving stochastic gradient algorithms that iteratively estimate the (generalized) eigenvector corresponding to the smallest (generalized) eigenvalue. Since the adaptive GEVD algorithm requires an estimate of the noise correlation matrix, we analyze its sensitivity with respect to the accuracy of this noise correlation matrix estimate, showing that the performance of the adaptive GEVD algorithm may be quite sensitive to deviations, especially for low SNR scenarios
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