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 time-frequency feature extraction method computing local auto-correlations 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.