Attention-based time–frequency aggregated tokens for sound event classification in basketball game

Abstract

Abstract With the rapid advancement of computer technology, its application in basketball game analysis has become a prominent research focus. Sound events in basketball games encapsulate critical game-related information. However, research on sound event classification in basketball games remains in its early stages. This research systematically analyzes the sound events in basketball games, and constructs an open-source dataset of sound events in basketball games, which is characterized by larger scale and more diverse categories. Furthermore, a deep learning-based sound event classification method integrating convolutional neural network with the transformer architecture is proposed. The proposed method employs a time–frequency feature aggregation approach to effectively capture both local time–frequency characteristics and global dependencies within audio signals, enabling precise sound event classification. Experimental results demonstrate that the proposed classification method significantly outperforms traditional methods across multiple evaluation metrics, offering a novel and effective solution for sound event analysis in basketball games. The proposed sound events dataset in basketball games will be available at https://github.com/holhouse/Sound-Event-Dataset-in-Basketball-Game .