Deep Learning Methods for Assessing Time‐Variant Nonlinear Signatures in Clutter Echoes

The biosonar systems of bats in families of horseshoe and Old‐World leaf‐nosed bats include peripheral dynamics where the outer ears undergo fast rotations and deformations during echo reception. These motions impart time‐variant linear and nonlinear effects on the received echoes. In the present study, we have investigated whether such time‐variant effects create discriminable and reliable signatures in clutter, i.e., echoes that are created by a superposition of reflections from multiple, unresolved scatterers. We have used a laboratory setup with artificial foliage that was agitated by fans to create large data sets of clutter echoes. These echoes were triggered by pulses with different time–frequency signatures (constant‐frequency, frequency‐modulated, and a compound of the two) and received by flexible biomimetic pinna that was actuated via strings to create two different motion shapes at five different motion speeds. Different deep‐learning architectures (ResNets, transformers, and a 2D convolutional neural network) were tested for their ability to classify the different motions based on single clutter echoes. The achieved performances (up to 97% overall correct classifications) demonstrated that the time‐variant signatures in the clutter echoes could form a reliable substrate for the encoding of sensory information that may provide functional advantages for navigating complex natural environments.