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Baseline Performance of Pre-trained Models on Movie Genre Classification from Spectrograms
Author(s)
Visutsak, Porawat
Treeraphapkajondet, Kavin
Sakphet, Visaroot
Nitinuntatip, Wachirawit
Satthong, Pawwinkan
Tongbai, Tanajak
Ongrungruaeng, Duongduen
Juntra, Atiwitch
Aiamlamai, Watcharaporn
Sungwanna, Issares
Phetrak, Prapaporn
Netisopakul, Ponrudee
Ryu, Keun Ho
Date Issued
April 1, 2025
Type
Article
Abstract
This study investigates the use of deep learning for classifying movie genres based on audio spectrograms. We construct a dataset of movie trailers, transform them into spectrograms, and label them by genre. Then, we utilize MATLAB's pre-trained convolutional neural networks (CNNs) for classification, comparing the performance of 9 different architectures, including MobileNet-v2, RestNet-18, DenseNet-201, Places365-GoogLeNet, VGG-16, VGG-19, Inception-RestNet-v2, Inception-v3, and NASANet-Mobile. We evaluated all models based on their ability to classify movie trailers into five genres: action, romance, drama, comedy, and thriller. Our results, based on accuracy and F1-score across genres, indicate that VGG16 achieves the highest overall performance with an accuracy of 86.27%, an F1-score of 86.69%, a recall of 86.87%, and a precision of 87.28%. This research demonstrates the potential of leveraging pre-trained CNNs, particularly VGG-16, for effcient and effective audio-based genre classification in movie trailers.
Citation
Ecti Transactions on Computer and Information Technology, 19(2), 364-378, 2025