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Genre Classification of Movie Trailers using Spectrogram Analysis and Machine Learning
Author(s)
Visutsak, Porawat
Pensiri, Fuangfar
Netisopakul, Ponrudee
Punsathit, Navapoom
Rojsuwan, Pongkorn
Phuthong, Maywadee
Khanthawat, Worapol
Prommanee, Wittawat
Jearaphan, Kanthong
Saekua, Tawichai
Anantaprueksa, Tichaporn
Kavalee, Napas
Sukkokee, Naravitch
Kingket, Nanticha
Teeravas, Prapassorn
Nimmonrat, Patchara
Yungyuen, Patcharada
Nurltes, Pattareeya
Wongpanti, Ratchanon
Hompangwhai, Sekson
Sutthikawee, Haritchaya
Siangchin, Apichai
Date Issued
January 1, 2024
Type
Conference Paper
Abstract
This study investigates the application of machine learning for movie trailer genre classification using spectrogram analysis. Spectrograms, visual representations of a signal's frequency content over time, are extracted from movie trailers. Convolutional Neural Networks (CNNs), known for their image recognition capabilities, are employed to analyze these spectrograms and identify patterns that distinguish between different genres. Our approach is compared to a Random Forest model for performance evaluation. The models are trained on a dataset of movie trailers categorized into five genres: action, romance, drama, comedy, and thriller. Librosa, a Python library, is utilized for audio pre-processing, while the overall training process is conducted within the Python environment. This paper explores the potential of machine learning in conjunction with spectrogram analysis for effective movie trailer genre classification.
Citation
2024 IEEE International Black Sea Conference on Communications and Networking Blackseacom 2024, 324-327, 2024