MADB: A Large-Scale Music Aesthetics Dataset with Professional and Multi-Dimensional Annotations
arXiv:2607.06929v1 Announce Type: cross Abstract: Music aesthetic assessment is a challenging yet underexplored problem, requiring models to capture fine-grained, multi-dimensional human perceptual judgments. Progress in this area has been limited by the lack of large-scale datasets with structured aesthetic annotations. We introduce MADB, a large-scale dataset and benchmark comprising 9,999 tracks annotated by 30 trained annotators. Each track is rated by around 10 annotators across 10 perceptual dimensions and one overall score, with additional textual comments for multimodal analysis. We es
The proliferation of AI models for content generation necessitates more sophisticated aesthetic evaluation metrics to improve model output and align with human perception.
This dataset provides a crucial foundation for developing AI that can understand and generate music with human-like aesthetic appeal, moving beyond mere technical correctness.
The availability of a large-scale, multi-dimensional music aesthetics dataset will accelerate research in AI music generation and evaluation, leading to more nuanced and artistically relevant AI outputs.
- · AI music generation companies
- · Music AI researchers
- · Content creators using AI music tools
- · AI models lacking aesthetic understanding
Improved aural aesthetics and emotional resonance in AI-generated music and soundscapes.
New business models emerging from AI-driven personalized music creation and curation based on aesthetic preferences.
Potential for AI to redefine musical genres and artistic expression through its understanding and manipulation of aesthetic dimensions.
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Read at arXiv cs.AI