Audio scene segmentation for video with generic content

In this paper, we present a content-adaptive audio texture based method to segment video into audio scenes. The audio scene is modeled as a semantically consistent chunk of audio data. Our algorithm is based on "semantic audio texture analysis." At first, we train GMM models for basic audio classes such as speech, music, etc. Then we define the semantic audio texture based on those classes. We study and present two types of scene changes, those corresponding to an overall audio texture change and those corresponding to a special "transition marker" used by the content creator, such as a short stretch of music in a sitcom or silence in dramatic content. Unlike prior work using genre specific heuristics, such as some methods presented for detecting commercials, we adaptively find out if such special transition markers are being used and if so, which of the base classes are being used as markers without any prior knowledge about the content. Our experimental results show that our proposed audio scene segmentation works well across a wide variety of broadcast content genres.