An ideal-observer model of human sound localization.

In recent years, a great deal of research within the field of sound localization has been aimed at finding the acoustic cues that human listeners use to localize sounds and understanding the mechanisms by which they process these cues. In this paper, we propose a complementary approach by constructing an ideal-observer model, by which we mean a model that performs optimal information processing within a Bayesian context. The model considers all available spatial information contained within the acoustic signals encoded by each ear. Parameters for the optimal Bayesian model are determined based on psychoacoustic discrimination experiments on interaural time difference and sound intensity. Without regard as to how the human auditory system actually processes information, we examine the best possible localization performance that could be achieved based only on analysis of the input information, given the constraints of the normal auditory system. We show that the model performance is generally in good agreement with the actual human localization performance, as assessed in a meta-analysis of many localization experiments (Best et al. in Principles and applications of spatial hearing, pp 14-23. World Scientific Publishing, Singapore, 2011). We believe this approach can shed new light on the optimality (or otherwise) of human sound localization, especially with regard to the level of uncertainty in the input information. Moreover, the proposed model allows one to study the relative importance of various (combinations of) acoustic cues for spatial localization and enables a prediction of which cues are most informative and therefore likely to be used by humans in various circumstances.

Modelling simultaneous echo waveform reconstruction and localization in bats.

Echolocating bats perceive the world through sound signals reflecting from the objects around them. In these signals, information is contained about reflector location and reflector identity. Bats are able to extract and separate the cues for location from those that carry identification information. We propose a model based on Wiener deconvolution that also performs this separation for a virtual system mimicking the echolocation system of the lesser spearnosed bat, Phyllostomus discolor. In particular, the model simultaneously reconstructs the reflected echo signal and localizes the reflector from which the echo originates. The proposed technique is based on a model that performs a similar task based on information from the frog's lateral line system. We show that direct application of the frog model to the bat sonar system is not feasible. However, we suggest a technique that does apply to the bat biosonar and indicate its performance in the presence of noise.