Automatic Scene Change Detection for Composed Speech and Music Sound Under Low SNR Noisy Environment

As the amount of available audiovisual data in digital formats is increasing, automatic scene change detection becomes paramount. Many studies have been proposed to treat it recently. Nevertheless, none of these techniques consider the audio signals under a low SNR noisy environment. In this paper, a hierarchical scene change-detection scheme is adopted to detect the scene change automatically under a low SNR noisy environment. The proposed algorithm contains three steps: A statistical model-based audio activity detection scheme that employs the likelihood ratio test is used to segment the audio signal into pure noise segments and noisy audio segments in the first step. In the second step, a noisy robustness feature is adopted to construct a K-Nearest Neighbor (KNN) based classifier and segments the noisy audio segments into speech and music segments. The feature we proposed is called the likelihood ratio crossing rate (LRCR) derived from the likelihood ratio waveform that is obtained in the first step. In last step, a novel speaker change detection based on modified Bayesian information criterion (BIC) is performed to detect the speaker change points for each speech segment. A series of tests were conducted showing the advantage of the proposed scheme. Furthermore, it also shows the robustness under a low SNR noisy environment.