In this report we present an overview of the approaches and techniques that are used in the task of automatic audio segmentation. Audio segmentation aims to find changing points in the audio content of an audio stream. Initially, we present the basic steps in an automatic audio segmentation procedure. Afterwards, the basic categories of segmentation algorithms, and more specific the unsupervised, the data-driven and the mixed algorithms, are presented. For each of the categorizations the segmentation analysis is followed by details about proposed architectural parameters, such us the audio descriptor set, the mathematical functions in unsupervised algorithms and the machine learning algorithms of data-driven modules. Finally a review of proposed architectures in the automatic audio segmentation literature appears, along with details about the experimenting audio environment (heading of database and list of audio events of interest), the basic modules of the procedure (categorization of the algorithm, audio descriptor set, architectural parameters and potential optional modules) along with the maximum achieved accuracy.