Identification of Rat Ultrasonic Vocalizations from Mix Sounds of a Robotic Rat in a Noisy Environment Social interaction between a robot and rats is important since the robot can generate reproducible social behaviors across trials. However, lacking internal state feedback from the rat makes current robotrat interaction a very preliminary level comparing with ratrat interaction. Previous biological studies showed that ultrasonic vocalizations (USVs) emitted by a rat are expressions of its internal emotional states, which therefore can be used as part of feedback for a robotrat interaction. The challenge is to accurately identify rat USVs in realtime from mix sounds generated by the robot in a noisy environment. To address these problems, we propose an SVMbased rat USVs identification method. This SVM method uses three types of features to represents the characteristics of mix sound and use these multidimensional features to identify rat USVs. Results show that our identification method has an accuracy of 84.29% with only 4.84% falsepositive rate. Furthermore, we carefully design the filter window length with respect to sound chunk length and use only one microphone to record the mix sound. All of these efforts are to reduce the calculation time to realize realtime identification. Eventually, the identification process can be executed within 3. 5ms, which definitely meet the realtime demand. This research lays the foundation of the feedback based interaction between rat and robot, and also shows promise in the study of ethology and the interaction between robot and animals.

Autonomous Speech Volume Control for Social Robots in a Noisy Environment Using Deep Reinforcement Learning This paper presents a novel approach to automatically adjusting the speech volume of a socially assistive humanoid robot to enhance the quality of humanrobot interactions. We apply the Deep Qlearning algorithm to enable the robot to adapt to the preferences of a user in the volume of the robotxe2x80x99s voice in social contexts. Subjective experiments were conducted to verify the validity of the proposed system. Twentythree human subjects had social conversations with humanoid robots across various noisy environments. Participants rated their perception of the robotsxe2x80x99 voices in terms of clearness and comfortability through a questionnaire. The results show that the robot equipped with our framework outperforms other experimental robots in trials. This study confirmed the effectiveness of the proposed autonomous speech volume control system for social robots communicating with people in noisy environments.