GenreGenius

Document Type

Abstract

Publication Date

Spring 5-1-2024

Abstract

Music, an intrinsic part of the human experience, resonates within us all. GenreGenius, a music recommender system, employs machine learning techniques to predict genres from real-world music datasets and offers personalized song recommendations to users based on their preferred genres, enriching musical exploration through the fusion of AI and music. This project applies unsupervised learning algorithms to explore the music datasets, revealing hidden patterns and structures that aid in grouping songs with similar characteristics. Following this exploration, supervised learning algorithms are employed to develop predictive models capable of classifying genres with precision based on audio features, such as tempo and beat. To deliver tailored recommendations, the project integrates content-based filtering, utilizing audio features to suggest songs aligned with users' preferred genres. Through the analysis of musical content shared with similar genres, the machine learning model enhances recommendation accuracy, providing users with tailored musical experiences that resonate with their tastes and preferences.

Comments

Completed as part of the Computer Science Senior Capstone Project.

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