Cheering Event Detection in Basketball Audio Stream Using Adaptive GMM Model and Low Rank Matrix Recovery

Abstract

In this paper, we propose a new method to detect cheering events in basketball audio streams by combining short time Fourier transform (STFT) bin strengths, adaptive Gaussian mixture model (GMM) and low rank matrix recovery (LRR) approach. First, we apply the STFT and then calculate pre-defined frequency bins based on a specific frequency range of cheering sounds. An adaptive GMM model is used as a classifier to detect cheering events. In addition, we also propose to apply a post processing approach based on the LRR and power spectral density (PSD) within specified frequency interval to reduce false alarms and to improve the performance of the system. The experimental results on Korean basketball audio database demonstrate that our proposed method can outperform other well-known methods and achieve high accuracy. Specifically, recall rate, precision rate and F value are, respectively, 92.38%, 91.29% and 91.83%.