Audio-based sports highlight detection by fourier local auto-correlations

In this paper, we present a novel methodology for sports highlight detection based on audio information. For processing the sounds of sports events, we propose a timefrequency feature extraction method computing local autocorrelations on complex Fourier values (FLAC). For highlights detection, we apply (complex) subspace method to the extracted FLAC features to detect the “exciting” scenes which occur sparsely in a background of “ordinary” periods. As an unsupervised learning algorithm, the subspace method maintains advantages that any prior knowledge and expensive-computation are not required. To evaluate the proposed method, we made experiments on a soccer match. The experimental results show the effectiveness of the proposed approach including robustness to environmental noise, low computation burden and promising performance.