Surveillance Audio Attention Model Based on Spatial Audio Cues
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Learning sound location from a single microphone
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Binaural source localization by joint estimation of ILD and ITD
IEEE Transactions on Audio, Speech, and Language Processing
The cocktail party robot: sound source separation and localisation with an active binaural head
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Tracking of multidimensional TDOA for multiple sources with distributed microphone pairs
Computer Speech and Language
Directional acoustic source orientation estimation using only two microphones
Digital Signal Processing
Speaker Tracking Using Recursive EM Algorithms
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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This paper addresses the problem of tracking multiple moving sources using binaural input. We observe that binaural cues are strongly correlated with source locations in time-frequency regions dominated by only one source. Based on this observation, we propose a novel tracking algorithm that integrates probabilities across reliable frequency channels in order to produce a likelihood function in the target space, which describes the azimuths of all active sources at a particular time frame. Finally, a hidden Markov model (HMM) is employed to form continuous tracks and automatically detect the number of active sources across time. Results are presented for up to three moving talkers in anechoic conditions. A comparison shows that our HMM model outperforms a Kalman filter-based approach in tracking active sources across time. Our study represents a first step in addressing auditory scene analysis with moving sound sources.