The paper explores the identification and categorization of music outliers to enhance music discovery and recommendation systems. The authors propose a definition of "Genuine" music outliers, which are complete songs that maintain an artist's typical musical structure while distinctly diverging in sound and style from their predominant body of work.
The key highlights and insights are:
The authors categorize outliers into five types: Error, Speech, Intro, Sound Effect, and Genuine. Genuine outliers are complete songs that exhibit unique characteristics compared to an artist's typical style.
The authors develop a dataset by manually identifying and labeling outliers from a sample of 20 artists in the Million Song Dataset. This dataset is used to evaluate the performance of an outlier detection algorithm.
The outlier detection algorithm based on the k-means clustering approach performs well on artists with a single dominant style, but struggles with artists exhibiting multiple styles. The authors suggest incorporating additional music features, considering the "Forms A Complete Song" and "Non-Adherence" constraints, and exploring alternative clustering models to improve the algorithm's performance.
The findings highlight the potential value of genuine music outliers in providing unique insights and contributing to a richer understanding of an artist's work. The authors suggest future research directions, such as integrating more music features, handling artists with multiple styles, and exploring other clustering models to enhance the detection of genuine music outliers.
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by Le Cai,Sam F... klokken arxiv.org 04-10-2024
https://arxiv.org/pdf/2404.06103.pdfDypere Spørsmål