A discriminative learning approach to probabilistic acoustic source localization

Sound source localization algorithms commonly include assessment of inter-sensor (generalized) correlation functions to obtain direction-of-arrival estimates. Here, we present a classification-based method for source localization that uses discriminative support vector machine-learning of correlation patterns that are indicative of source presence or absence. Subsequent probabilistic modeling generates a map of sound source presence probability in given directions. Being data-driven, the method during training adapts to characteristics of the sensor setup, such as convolution effects in non-free-field situations, and to target signal specific acoustic properties. Experimental evaluation was conducted with algorithm training in anechoic single-talker scenarios and test data from several reverberant multi-talker situations, together with diffuse and real-recorded background noise, respectively. Results demonstrate that the method successfully generalizes from training to test conditions. Improvement over the best of five investigated state-of-the-art angular spectrum-based reference methods was on average about 45% in terms of relative F-measure-related error reduction.