Learn More
Conventional time-delay estimators exhibit dramatic performance degradations in the presence of multipath signals. This limits their application in reverberant enclosures, particularly when the signal of interest is speech and it may not possible to estimate and compensate for channel effects prior to time-delay estimation. This paper details an alternative(More)
Electronically steerable arrays of microphones have a variety of uses in speech data acquisition systems. Applications include teleconferencing, speech recognition and speaker identiication, sound capture in adverse environments, and biomedical devices for the hearing impaired. An array of microphones has a number of advantages over a single-microphone(More)
In most microphone array applications, it is essential to localize sources in a noisy, reverberant environment. It has been shown that computing the steered response power (SRP) is more robust than faster, two-stage, direct time-difference of arrival methods. The problem with computing SRP is that the SRP space has many local maxima and thus(More)
The linear intersection (LI) estimator, a closed-form method for the localization of source positions given sensor array time-delay estimate information, is presented. The LI estimator is shown to be robust and accurate, to closely model the search-based ML estimator, and to outperform a benchmark algorithm. The computational complexity of the LI estimator(More)
Microphone arrays often operate in the near field, which complicates the problem of determining a source location from time-difference-of-arrival (TDOA) measurements typically derived from generalized cross-correlation functions. Each TDOA satisfies the equation of a hyperboloid in space and methods have been developed to either solve for intersecting(More)
A method for tracking the positional estimates of multiple talkers in the operating region of an acoustic microphone array is presented. Initial talker location estimates are provided by a time-delay-based localization algorithm. These raw estimates are spatially smoothed by a Kalman lter derived from a set of potential source motion models. Data(More)
Typically, parameter estimation for a hidden Markov model (HMM) is performed using an expectation-maximization (EM) algorithm with the maximum-likelihood (ML) criterion. The EM algorithm is an iterative scheme which is well-deened and numerically stable, but convergence may require a large number of iterations. For speech recognition systems utilizing large(More)