A Unified Audio Analysis Framework For Movie Genre Classification Using Movie Trailers

The audio content of the movie trailers carries various prominent characteristics that can be exploited to predict the genres of the movie. None of the previous approaches have focused on extracting the audio features from the trailers using a clustering-based unsupervised learning approach followed by distance-based supervised learning. Hence, in this paper, we propose a novel framework for movie genre classification using audio features of movie trailers. Movie trailers belonging to the five most generic and popular genres (i.e., Action, Romance, Horror, Science Fiction, and Comedy) are considered in the work. The proposed AFAnet architecture is trained on a total of 78 features including 68 audio features and 10 distance-based features extracted after the k-means clustering on the audio chunks. The cross-dataset validation is performed using the standard LMTD dataset to validate the performance of our proposed framework. The results obtained depict that our model has performed excellently and show the robustness of the proposed approach.