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Adaptive Distance Measures for Exploration and Structuring of Music Collections
Music similarity plays an important role in many Music Information Retrieval applications. However, it has many facets and its perception is highly subjective -- very much depending on a person's background or retrieval goal. This paper presents a generalized approach to modeling and learning individual distance measures for comparing music pieces based on multiple facets that can be weighted. The learning process is described as an optimization problem guided by generic distance constraints. Three application scenarios with different objectives exemplify how the proposed method can be employed in various contexts by deriving distance constraints either from domain-specific expert information or user actions in an interactive setting.
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