ikeda7/music-ai-generator

This is a personal research project with zero adoption signals (0 stars, 0 forks, 0 activity velocity, 215 days old). It applies well-established Transformer-based sequence modeling to music generation—a space with numerous prior art projects (e.g., Music Transformer by Google, MuseNet, Jukebox, etc.). The specific contribution—scale filtering via music theory rules—is an incremental refinement rather than a novel approach. The project appears to be a tutorial-grade implementation combining standard techniques (Transformer encoder-decoder, MIDI tokenization, existing datasets) without evidence of novel architectural choices, superior performance, or community traction. Frontier labs (OpenAI, Google, Anthropic) are actively building music generation systems and have already deployed more sophisticated models; this would be trivial for them to replicate or exceed. The lack of any stars, forks, or recent activity indicates no user adoption or external validation. As a reference implementation, it has educational value but zero defensibility against either small competitors or frontier labs.