Baseline Performance of Pre-trained Models on Movie Genre Classification from Spectrograms

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dc.contributor.authorVisutsak, Porawat
dc.contributor.authorTreeraphapkajondet, Kavin
dc.contributor.authorSakphet, Visaroot
dc.contributor.authorNitinuntatip, Wachirawit
dc.contributor.authorSatthong, Pawwinkan
dc.contributor.authorTongbai, Tanajak
dc.contributor.authorOngrungruaeng, Duongduen
dc.contributor.authorJuntra, Atiwitch
dc.contributor.authorAiamlamai, Watcharaporn
dc.contributor.authorSungwanna, Issares
dc.contributor.authorPhetrak, Prapaporn
dc.contributor.authorNetisopakul, Ponrudee
dc.contributor.authorRyu, Keun Ho
dc.date.accessioned2026-08-06T10:50:56Z
dc.date.available2026-08-06T10:50:56Z
dc.date.issued2025-04-01
dc.description.abstractThis 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.
dc.identifier.citationEcti Transactions on Computer and Information Technology, 19(2), 364-378, 2025
dc.identifier.doi10.37936/ecti-cit.2025192.259990
dc.identifier.issn22869131
dc.identifier.other2-s2.0-105005162280
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/16921
dc.sourceEcti Transactions on Computer and Information Technology
dc.subjectAudio Spectrograms
dc.subjectDeep Learning
dc.subjectMATLAB
dc.subjectMovie Genre Classification
dc.subjectPre-trained CNNs
dc.titleBaseline Performance of Pre-trained Models on Movie Genre Classification from Spectrograms
dc.typeArticle

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