Multiple Sound Source Localisation in Reverberant Environments Inspired by the Auditory Midbrain
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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
Biomimetic binaural sound source localisation with ego-noise cancellation
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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This paper proposes a biologically inspired and technically implemented sound localization system to robustly estimate the position of a sound source in the frontal azimuthal half-plane. For localization, binaural cues are extracted using cochleagrams generated by a cochlear model that serve as input to the system. The basic idea of the model is to separately measure interaural time differences and interaural level differences for a number of frequencies and process these measurements as a whole. This leads to two-dimensional frequency versus time-delay representations of binaural cues, so-called activity maps. A probabilistic evaluation is presented to estimate the position of a sound source over time based on these activity maps. Learned reference maps for different azimuthal positions are integrated into the computation to gain time-dependent discrete conditional probabilities. At every timestep these probabilities are combined over frequencies and binaural cues to estimate the sound source position. In addition, they are propagated over time to improve position estimation. This leads to a system that is able to localize audible signals, for example human speech signals, even in reverberating environments