Robust auditory localization using probabilistic inference and coherence-based weighting of interaural cues.

Abstract

Robust sound source localization is performed by the human auditory system even in challenging acoustic conditions and in previously unencountered, complex scenarios. Here a computational binaural localization model is proposed that possesses mechanisms for handling of corrupted or unreliable localization cues and generalization across different acoustic situations. Central to the model is the use of interaural coherence, measured as interaural vector strength (IVS), to dynamically weight the importance of observed interaural phase (IPD) and level (ILD) differences in frequency bands up to 1.4 kHz. This is accomplished through formulation of a probabilistic model in which the ILD and IPD distributions pertaining to a specific source location are dependent on observed interaural coherence. Bayesian computation of the direction-of-arrival probability map naturally leads to coherence-weighted integration of location cues across frequency and time. Results confirm the model's validity through statistical analyses of interaural parameter values. Simulated localization experiments show that even data points with low reliability (i.e., low IVS) can be exploited to enhance localization performance. A temporal integration length of at least 200 ms is required to gain a benefit; this is in accordance with previous psychoacoustic findings on temporal integration of spatial cues in the human auditory system.