| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 413310 | Robotics and Autonomous Systems | 2010 | 12 Pages |
This paper deals with the problem of localizing and tracking a moving speaker over the full range around the mobile robot. The problem is solved by taking advantage of the phase shift between signals received at spatially separated microphones. The proposed algorithm is based on estimating the time difference of arrival by maximizing the weighted cross-correlation function in order to determine the azimuth angle of the detected speaker. The cross-correlation is enhanced with an adaptive signal-to-noise estimation algorithm to make the azimuth estimation more robust in noisy surroundings. A post-processing technique is proposed in which each of these microphone-pair determined azimuths are further combined into a mixture of von Mises distributions, thus producing a practical probabilistic representation of the microphone array measurement. It is shown that this distribution is inherently multimodal and that the system at hand is non-linear. Therefore, particle filtering is applied for discrete representation of the distribution function. Furthermore, the two most common microphone array geometries are analysed and exhaustive experiments were conducted in order to qualitatively and quantitatively test the algorithm and compare the two geometries. Also, a voice activity detection algorithm based on the before-mentioned signal-to-noise estimator was implemented and incorporated into the existing speaker localization system. The results show that the algorithm can reliably and accurately localize and track a moving speaker.
Research highlights► a voice activity detector is integrated into speaker localization framework ► square array shows smaller error sensitivity than the Y array ► speaker azimuth is calculated in a robust and computationally undemanding manner ► number of cross-correlation evaluations is equal to the number of microphone pairs ► the algorithm performance is verified with an accurate laser leg-tracking algorithm
