This work deals with the problem of automatic temporal segmentation of a video into elemen-tary semantic units known as scenes. Its novelty lies in the use of high-level audio information, in the form of audio events, for the improvement of scene segmentation performance. More specifically, the proposed technique is built upon a recently proposed audio-visual scene segmentation approach that involves the construction of multiple scene transition graphs (STGs) that separately exploit information coming from different modalities. In the extension of the latter approach presented in this work, audio event detection results are introduced to the definition of an audio-based scene transition graph, while a visual-based scene transition graph is also defined independently. The results of these two types of STGs are subsequently combined. The results of the application of the proposed technique to broadcast videos demonstrate the usefulness of audio events for scene segmentation and highlight the importance of introducing additional high-level information to the scene segmentation algorithms.