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A Comprehensive Survey for Evaluation Methodologies of AI-Generated Music
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In recent years, AI-generated music has made significant progress, with several models performing well in multimodal and complex musical genres and scenes. While objective metrics can be used to evaluate generative music, they often lack interpretability for musical evaluation. Therefore, researchers often resort to subjective user studies to assess the quality of the generated works, which can be resource-intensive and less reproducible than objective metrics. This study aims to comprehensively evaluate the subjective, objective, and combined methodologies for assessing AI-generated music, highlighting the advantages and disadvantages of each approach. Ultimately, this study provides a valuable reference for unifying generative AI in the field of music evaluation.
Forward citations
Cited by 3 Pith papers
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Agogic: Performance-Timed Music Tokens for LLM-Native Text-to-Symbolic-Music Generation
Controlled experiments show that a 10ms performance-timed token stream lowers Frechet Music Distance roughly twofold versus beat-grid tokens, across model sizes from 0.8B to 27B.
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Workflow-Based Evaluation of Music Generation Systems
A single-producer workflow evaluation of eight music AI tools finds they work as idea and sound generators but not as complete composers, and proposes a reusable framework.
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Missing Melodies: AI Music Generation and its "Nearly" Complete Omission of the Global South
A survey of over one million hours of music datasets and 244 papers finds that Global South music accounts for only 14.6% of training data and is nearly absent from research authorship.
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