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An Unsupervised Hybrid Approach for Online Detection of Sound Scene Changes in Broadcast Content

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In this paper we describe an online system for broadcast content, which can detect sound scene changes with high accuracy. The system is unsupervised and does not require prior information on the segment classes. A scene change probability score is computed for each frame of the signal using a hybrid approach combining a model-based (Gaussian Mixture Model) with a distance-based (Hotelling’s T2-Statistic) segmentation method. The mixture model parameters are adapted online using the previous frames of the signal. Experiments on real recordings show that we can achieve more than 85% correct segment change detection with only 16% false detections.

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