Genre Classification of Movie Trailers using Spectrogram Analysis and Machine Learning - Porawat Visutsak; Fuangfar Pensiri; Ponrudee Netisopakul; Navapoom Punsathit; Pongkorn Rojsuwan; Maywadee Phuthong; Worapol Khanthawat; Wittawat Prommanee; Kanthong Jearaphan; Tawichai Saekua; Tichaporn Anantaprueksa; Napas Kavalee; et al. (My University, 2024)
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.
Keywords: Generative Adversarial Networks and Image Synthesis; Media Influence and Health; Cinema and Media Studies