Investigating the impact of biomimetic pinna shape variations on clutter echoes received from natural environments

Abstract

Bat species navigating dense vegetation based on biosonar have to obtain the necessary sensory information from “clutter echoes”, i.e., echoes that are superpositions of contributions from many reflecting facets (e.g., leaves) and hence have highly unpredictable waveforms. Prior results have suggested that pinna motions could aid in direction-finding tasks based on deterministic echo patterns. This raises the question whether varying pinna shapes could also have a function significance for challenging biosonar tasks performed on clutter echoes. As a first, task-independent step to test this hypothesis it has been investigated whether different pinna shapes have a consistent effect on clutter echoes despite the random nature of these signals. This was accomplished using a dedicated laboratory setup that produced large amounts of uncorrelated clutter echo data by agitating an artificial foliage with fans between echoes. Deep learning methods were then applied to identify the pinna shape that received a given clutter echo. A two-dimensional convolutional neural network operating on spectrograms achieved 90% validation accuracy in this task. This finding demonstrates that even small pinna deformations can impart consistent effects on the clutter echoes. Ongoing research is directed at analyzing the nature of the signal properties that the successful classifications were based on.