How Far Can Chord-Symbol Time-Series Adaptation Carry Genre Identity? Capabilities and Boundaries in Multi-Genre Chord-Symbol Modeling
arXiv:2606.07334v1 Announce Type: cross Abstract: Harmony is a compact symbolic layer where mathematical pitch relations, acoustic consonance, and musical convention meet. This report treats chord-symbol sequences not as a complete representation of music, but as an interpretable, controllable time series for genre-local harmonic modeling. Starting from a frozen pop-jazz Music Transformer checkpoint, I evaluate how far small adaptation interfaces can extend the model to eleven target genres: blues, bossa nova, Bach chorales, country, electronic, folk, funk, gospel, hip-hop, R&B/soul, and rock.
The proliferation of foundational AI models and the increasing sophistication of machine learning in creative domains necessitate explorations into fine-tuning capabilities for specific artistic expressions.
This research contributes to understanding the adaptability of advanced AI models in nuanced creative tasks, crucial for developing more versatile and domain-specific AI applications in music generation and analysis.
It highlights the potential for pre-trained AI models to be efficiently adapted to new stylistic contexts with minimal intervention, expanding their utility across diverse creative fields.
- · AI music generation platforms
- · Music producers
- · AI researchers
- · Entertainment industry
Improved AI models for music generation across various genres, potentially automating aspects of musical composition.
Increased accessibility of AI-powered creative tools for musicians and artists, lowering barriers to sophisticated music production.
Evolution of new musical forms and genres influenced or co-created by advanced AI systems, blurring traditional lines of authorship.
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