Abstract: Provided are an environmental sound pass-through method and apparatus applied to VR, a device and a storage medium. The method includes the steps below. Environmental audio information of an ambient environment is acquired by a microphone on a VR device based on a see-through mode of the VR device. The environmental audio information is located to determine sound source position information according to the time difference of the environmental audio information reaching microphones on the VR device, and a filtering process is performed on the environmental audio information to determine filtered audio information. Directional pickup is performed on the filtered audio information to determine to-be-processed audio information according to the sound source position information. A signal amplitude and pass-through loudness of the to-be-processed audio information are adjusted to determine target audio information, and the target audio information is played by a speaker of the VR device.

Abstract: The invention provides a classroom cognitive load detection system belonging to the field of education informationization, which includes the following. A task completion feature collecting module records an answer response time and a correct answer rate of a student when completing a task. A cognitive load self-assessment collecting module quantifies and analyzes a mental effort and a task subjective difficulty by a rating scale. An expression and attention feature collecting module collects a student classroom performance video to obtain a face region through a face detection and counting a smiley face duration and a watching duration of the student according to a video analysis result. A feature fusion module fuses aforesaid six indexes into a characteristic vector. A cognitive load determining module inputs the characteristic vector to a classifier to identify a classroom cognitive load level of the student.

Abstract: The invention provides a classroom cognitive load detection system belonging to the field of education informationization, which includes the following. A task completion feature collecting module records an answer response time and a correct answer rate of a student when completing a task. A cognitive load self-assessment collecting module quantifies and analyzes a mental effort and a task subjective difficulty by a rating scale. An expression and attention feature collecting module collects a student classroom performance video to obtain a face region through a face detection and counting a smiley face duration and a watching duration of the student according to a video analysis result. A feature fusion module fuses aforesaid six indexes into a characteristic vector. A cognitive load determining module inputs the characteristic vector to a classifier to identify a classroom cognitive load level of the student.