A learning-based approach to robust binaural sound localization

Sound source localization is an important feature designed and implemented on robots and intelligent systems. Like other artificial audition tasks, it is constrained to multiple problems, notably sound reflections and noises. This paper presents a sound source azimuth estimation approach in reverberant environments. It exploits binaural signals in a humanoid robotic context. Interaural Time and Level Differences (ITD and ILD) are extracted on multiple frequency bands and combined with a neural network-based learning scheme. A cue filtering process is used to reduce the reverberations effects. The system has been evaluated with simulation and real data, in multiple aspects covering realistic robot operating conditions, and was proven satisfying and effective as will be shown and discussed in the paper.

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