Piano Accompaniment Generation
This project explores what mixed AI models can learn from real piano accompaniment performances, including musical patterns, coherence based on song structure, and expressive articulation by the pianist. We are investigating what can be achieved using AI models and a very limited dataset.
Project-generated accompaniment demos are listed below for research review.
Copyright notice: I do not claim ownership of any third-party song composition or recording used as an evaluation reference. The project-generated MIDI accompaniment outputs are included for academic and research discussion. Please do not redistribute third-party source material from these demos.
Demos
The following demos use the Chinese pop song Ren Jian Yan Huo by singer Cheng Xiang as an academic evaluation reference. Vocals are included solely to show alignment with the piano accompaniment; the vocal track is intentionally reduced by 15 dB to highlight the piano, and no other post-processing is applied. The training dataset focuses on Chinese pop, so Ren Jian Yan Huo was selected to match that data bias.
Selected project-generated accompaniment tracks (MP3) are listed with corresponding MIDI files. I am not a musician, so the comments below are technical rather than artistic, focusing on factors such as alignment with training data statistics, per-song pattern repetition, and whether patterns match their intended roles.
All MIDI files in these demos are time-aligned to the vocal track and preserve the full articulation from the original live pianist performance. Because the performances are unquantized, note onsets will not fall exactly on a rigid time grid—this is expected and reflects natural human timing and expression.
Sample of Earlier Style Support
This is a sample from an earlier version. Each style uses more pattern options, which can sound more creative, but it often lacks consistency across sections and phrases.
| MIDI
12 Styles Currently Supported
Currently we support the following style keywords, selected to reduce overlap and to have sufficient samples in the dataset for evaluation.
Publications
To be updated.