Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Role of head pose estimation in speech acquisition from distant microphones
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
A robust method to extract talker azimuth orientation using a large-aperture microphone array
IEEE Transactions on Audio, Speech, and Language Processing
Acoustic source localization and tracking using track before detect
IEEE Transactions on Audio, Speech, and Language Processing
Two-microphone multi-speaker localization based on a Laplacian Mixture Model
Digital Signal Processing
Sound direction estimation using an artificial ear for robots
Robotics and Autonomous Systems
Multiple model target tracking with variable rate particle filters
Digital Signal Processing
Binaural Tracking of Multiple Moving Sources
IEEE Transactions on Audio, Speech, and Language Processing
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A simple physical model consisting of a point source displaced from its center of rotation, in combination with a directivity model that includes backwards emitted energy, is considered for the problem of estimating the orientation of a directional acoustic source. Such a problem arises, for instance, in voice-commanded devices in a smart room and is usually tackled with a large or distributed microphone array. We show, however, that when the time difference of arrival is also taken into account, a small array of only two microphones is sufficiently robust against unaccounted factors such as microphone directivity variation and mild reverberation. This is shown by comparing predicted and measured values of binaural cues, and by using them and pairwise frame energies as inputs for an artificial neural network (ANN) in order to estimate source orientation.