Abstract: Disclosed are a multi-speaker overlapping voice detection method and a system. The method includes: obtaining a voice to be detected, and removing silence from the voice to be detected is removed; extracting a feature of the voice to be detected after silence removal to obtain a voice feature of the voice to be detected; and inputting the voice feature into an overlapping voice detection model to obtain an overlapping speaker number corresponding to the voice to be detected output by the overlapping voice detection model. The overlapping voice detection model is obtained by supervised training based on a voice feature of a sample voice and a corresponding label of the overlapping speaker number, extracts an embedding of the voice feature, and classifies the overlapping speaker number to obtain the overlapping speaker number of the voice to be detected based on the extracted speaker embedding.

Abstract: The various implementations described herein include techniques and apparatuses for multi-channel biopotential signal acquisition, biopotential signal pre-processing, and adaptive signal conditioning. In one aspect, an analog frontend circuit includes an instrumentation amplifier (INA) and an analog-to-digital circuit (ADC) in a forward signal path. A digital circuit receives input from the ADC. An adaptive baseline tracking and compensation circuit tracks and compensates moving motion artifact driven changes. A power line interference (PLI) detection and compensation circuit tracks a desired number of PLI harmonics, and magnitude, phase and frequency for the PLI harmonics in real time. A digital-to-analog converter (DAC) circuit combines output of the adaptive baseline tracking and compensation circuit and the PLI detection and compensation circuit to output an analog output. A passive filter receives the analog output and drives an analog compensation signal at an input of the INA.

Abstract: The present invention discloses construction method and system of a descriptive model of classroom teaching behavior events. The construction method includes steps as the followings: acquiring classroom teaching video data to be trained; dividing the classroom teaching video data to be trained into multiple events according to utterances of a teacher by using a voice activity detection technology; and performing multi-modal recognition on all events by using multiple artificial intelligence technologies to divide the events into sub-events in multiple dimensions, establishing an event descriptive model according to the sub-events, and describing various teaching behavior events of the teacher in a classroom. The present invention divides a classroom video according to voice, which can ensure the completeness of the teacher's non-verbal behavior in each event to the greatest extent.

Abstract: Disclosed are a multi-speaker overlapping voice detection method and a system. The method includes: obtaining a voice to be detected, and removing silence from the voice to be detected is removed; extracting a feature of the voice to be detected after silence removal to obtain a voice feature of the voice to be detected; and inputting the voice feature into an overlapping voice detection model to obtain an overlapping speaker number corresponding to the voice to be detected output by the overlapping voice detection model. The overlapping voice detection model is obtained by supervised training based on a voice feature of a sample voice and a corresponding label of the overlapping speaker number, extracts an embedding of the voice feature, and classifies the overlapping speaker number to obtain the overlapping speaker number of the voice to be detected based on the extracted speaker embedding.

Abstract: The present invention discloses construction method and system of a descriptive model of classroom teaching behavior events. The construction method includes steps as the followings: acquiring classroom teaching video data to be trained; dividing the classroom teaching video data to be trained into multiple events according to utterances of a teacher by using a voice activity detection technology; and performing multi-modal recognition on all events by using multiple artificial intelligence technologies to divide the events into sub-events in multiple dimensions, establishing an event descriptive model according to the sub-events, and describing various teaching behavior events of the teacher in a classroom. The present invention divides a classroom video according to voice, which can ensure the completeness of the teacher's non-verbal behavior in each event to the greatest extent.