# Multi-Scale Audio Spectrogram Transformer for Classroom Teaching Interaction Recognition
## 1 Introduction  
The rapid development of deep learning has contributed to a level of intelligence in the modern education industry; with the help of artificial intelligence algorithms, teachers can be assisted in recording and analyzing classroom conditions [1; 2; 3]. The degree of teacher-student interaction in the classroom can be reflected in the classroom behaviors of students and teachers, such as students taking the initiative to ask questions, passively answering teachers' questions, group discussions and so on. However, due to a large number of students and the complexity of the scene in traditional classrooms, as well as the hidden nature of teacher-student interactions, it is difficult and unstable to effectively locate students who are interacting. Because of the above issues, mining classroom audio features and using salient voice interaction data of teachers and students within the classroom helps to understand the state of the classroom and improve the effectiveness of classroom teaching and learning. A productive and active classroom is often characterized by the presence of multiple voices, such as students interacting with the teacher in question-and-answer sessions, and students interacting with each other in discussions. A classroomthat bores students often has long periods of teacher lectures or even silence within it, and students' classroom effectiveness is significantly reduced when effective interaction is lacking. Flanders' interactive system [4] has the class recorded manually, which not only tends to lead to missing records but also consumes a lot of human resources. Therefore, combining intelligent recognition algorithms to more efficiently identify the status of classroom voice interactions helps to address the lack of automated classroom analysis tools for traditional teaching evaluation. Sound is one of the bridges through which humans communicate information, and audio recognition algorithms enable tasks such as speech emotion analysis [5; 6], environmental audio classification [7; 8], etc. Earlier methods extracted low-level descriptors such as Mel frequency, spectral coefficients, spectral centroids and spectral flatness, and combined them with methods such as support vector machines (SVM) and k-nearest neighbor rules (KNN) to complete sound classification [9; 10; 11]. Additionally, with the improvement of computer performance, Convolutional Neural Network (CNN) based feature extraction methods such as AlexNet [12], VGGNet [13] and ResNet [14] have been proposed to improve the accuracy of image recognition and feature extraction. The subsequent CNN is widely used to learn representations from audio spectrograms for end-to-end modeling [15; 16]. From the perspective of algorithm, although the above methods have been applied in different fields, the methods based on convolutional neural networks have a certain degree of limitation and lack modeling of temporal features in the audio recognition process. Following this, researchers proposed a recognition method based on convolutional recurrent neural networks (CRNN) and introduced an attention mechanism [17; 18; 19; 20; 21; 22; 23]. Audio features are extracted by a CNN, which is used as input to a recurrent neural network (RNN) [24], and the attention mechanism finishes the weighting of the features. This not only enhances the temporal modeling of the spectrograms, but CRNN is also able to attend to the important feature maps in the spectrograms. However, due to the nature of convolutional computing, the process of feature extraction only focuses on local information and lacks global modeling of spectral features. In [25], the Audio Spectrograph Transformer (AST) was introduced, which is the first non-convolutional, purely attention-based audio classification model that captures the global context even at the lowest level.