Build a training interface to install the bat's echolocation skills in humans

Bats use a sophisticated ultrasonic sensing method called echolocation to recognize the environment. Recently, it has been reported that sighted human participants with no prior experience in echolocation can improve their ability to perceive the spatial layout of various environments through training to listen to echoes (Norman, et al., 2021). In this study, we developed the new training system for human echolocation using the eye-tracker. Binaural echoes of consecutive downward linear FM pulses that were inspired by feeding strategies of echolocating bats were simulated using the wave equation finite difference time domain method. The virtual echoes were presented to the sighted subject in response to his or her eye movements on the monitor. The latency from eye gazing to the echo presentation wasn't audible delay to perceive. In a preliminary experiment in which the participants were asked to identify the shapes of the hidden target, the participants were found to concentrate their gaze on the edges of the hidden target on the monitor. We will conduct a psycho-acoustical experiment to examine the learning process of human echolocation in a shape-identification task, which will lead to device development in the field of welfare engineering.

Joint Analysis of Acoustic Scenes and Sound Events Based on Multitask Learning with Dynamic Weight Adaptation

Acoustic scene classification (ASC) and sound event detection (SED) are major topics in environmental sound analysis. Considering that acoustic scenes and sound events are closely related to each other, the joint analysis of acoustic scenes and sound events using multitask learning (MTL)-based neural networks was proposed in some previous works. Conventional methods train MTL-based models using a linear combination of ASC and SED loss functions with constant weights. However, the performance of conventional MTL-based methods depends strongly on the weights of the ASC and SED losses, and it is difficult to determine the appropriate balance between the constant weights of the losses of MTL of ASC and SED. In this paper, we thus propose dynamic weight adaptation methods for MTL of ASC and SED based on dynamic weight average and multi--focal loss to adjust the learning weights automatically. Evaluation experiments using parts of the TUT Acoustic Scenes 2016/2017 and TUT Sound Events 2016/2017 are conducted, and we show that the proposed methods improve the scene classification and event detection performance characteristics compared with the conventional MTL-based method. We then investigate how the learning weights of ASC and SED tasks dynamically adapt as the model training progresses.