Unsupervised Methods for Audio Classification from Lecture Discussion Recordings

Abstract

Time allocated for lecturing and student discussions is an important indicator of classroom quality assessment. Automated classification of lecture and discussion recording segments can serve as an indicator of classroom activity in a flipped classroom setting. Segments of lecture are primarily the speech of the lecturer, while segments of discussion include student speech, silence and noise. Multiple audio recorders simultaneously document all class activities. Recordings are coarsely synchronized to a common start time. We note that the lecturer's speech tends to be common across recordings, but student discussions are captured only in the nearby device(s). Therefore, we window each recording at 0.5 s to 5 s duration and 0.1 s analysis rate. We compute the normalized similarity between a given window and temporally proximate window segments in other recordings. Histogram plot categorizes higher similarity windows as lecture and lower ones as discussion. To improve the classification performance, high energy lecture windows and windows with very high and very low similarity are used to train a supervised model, in order to regenerate the classification results of remaining windows. Experimental results show that binary classification accuracy improves from 96.84% to 97.37%.

Learning Analytics from Spoken Discussion Dialogs in Flipped Classroom

“…Each recording contains discussion speech as well as broadcasted information such as general class announcements and solutions to problems. Since we are more interested in the portions of the audio that only contain group discussions, an unsupervised classification of lecture and discussion is achieved by using a customized audio processing technique [34]. In designing the classification algorithm, we aim to fully leverage the simultaneous recordings from the devices placed around the classroom.…”

Multimodal Classification of Teaching Activities from University Lecture Recordings

“…Audio-based classification has also been used for assessing time devoted to lecturing and student discussion, specifically in a flipped classroom setting [15]. Similar to DART, in the latter cited work, the authors use multiple audio recorders to detect segments of lecture that are primarily the lecturer's speech, while segments of discussion comprise students' speech, silence and noise.…”