Emotion-Based Music Classification using the Fuzzy Inference System (FIS) Mamdani Algorithm
DOI:
https://doi.org/10.31315/cip.v2i1.10763Abstract
This article presents a comprehensive research endeavor focusing on the classification of music based on emotions, utilizing the Fuzzy Inference System (FIS) Mamdani algorithm. The primary objective of this study is to accurately categorize music into distinct emotional moods by employing the FIS algorithm in conjunction with carefully selected audio features. Additionally, the research endeavors to analyze the impact of music on playlists and user preferences concerning emotional content. The FIS Mamdani algorithm, with its ability to effectively handle uncertainties inherent in music emotion classification, is meticulously implemented through the four fundamental steps of fuzzification, rule evaluation, aggregation, and defuzzification, incorporating linguistic variables representing various emotional states like happiness, sadness, anger, and relaxation. By employing mutual information-based feature selection, the research enhances the accuracy and efficiency of music emotion classification by identifying and leveraging the most pertinent audio features, including danceability, energy, valence, and tempo, to determine the emotional qualities of music more precisely. The performance evaluation based on a confusion matrix reveals promising results, showcasing an average accuracy of 75%, precision of 85%, and recall of 81%, thereby affirming the efficacy of the FIS Mamdani algorithm in proficiently categorizing music emotions. The implications of this study are wide-ranging, suggesting potential applications in personalized music recommendation systems, elevating user experiences on music streaming platforms, and exploring extensions to incorporate other psychological models of emotions. In essence, this research constitutes a significant stride towards advancing emotion-based music recommendation systems, consequently augmenting the digital music landscape to cater to diverse user preferences worldwide.References
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