Difference between revisions of "2024:Symbolic Music Generation Results"
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| style="vertical-align:bottom;" | Chart-Accompaniment | | style="vertical-align:bottom;" | Chart-Accompaniment | ||
| − | | style="vertical-align:bottom;" | | + | | style="vertical-align:bottom;" | [https://futuremirex.com/portal/wp-content/uploads/2024/11/chart_accomp_2024_ISMIR_LBD.pdf PDF] |
| style="vertical-align:bottom;" | BART | | style="vertical-align:bottom;" | BART | ||
| A BART model generating piano accompaniments using beat-based tokenization. | | A BART model generating piano accompaniments using beat-based tokenization. | ||
Revision as of 11:07, 11 November 2024
Submissions
| Team | Extended Abstract | Methods | Methodology |
|---|---|---|---|
| Chart-Accompaniment | BART | A BART model generating piano accompaniments using beat-based tokenization. | |
| AccoMontage (BL-1) | Style Transfer | A hybrid algorithm generating piano accompaniments by rule-based search and music representation learning. | |
| Whole-Song-Gen (BL-2) | DDPM | A denoising diffusion probabilistic model (DDPM) generating piano accompaniments as piano-roll images | |
| Compose-&-Embesslish (BL-3) | Transformer | A Transformer-based architecture generating piano performances in beat-based event sequences. |
Results
| Team | Subjective Evaluation | Objective Evaluation | |||
|---|---|---|---|---|---|
| Coherecy ↑ | Naturalness ↑ | Creativity ↑ | Musicality ↑ | NLL ↓ | |
| Chart-Accompaniment | 1.92 ± 0.11d | 1.87 ± 0.10c | 2.62 ± 0.13c | 2.01 ± 0.11c | 4.12 ± 0.12c |
| AccoMontage (BL-1) | 3.77 ± 0.11a | 3.59 ± 0.11a | 3.65 ± 0.11a | 3.63 ± 0.12a | 2.48 ± 0.07a |
| Whole-Song-Gen (BL-2) | 3.59 ± 0.11b | 3.24 ± 0.11b | 3.66 ± 0.10a | 3.47 ± 0.13b | 2.87 ± 0.08b |
| Compose-&-Embesslish (BL-3) | 3.39 ± 0.10c | 3.38 ± 0.12b | 3.13 ± 0.10b | 3.36 ± 0.11b | 7.41 ± 0.07d |
Note: Results are reported in the form of mean ± sems (sem refers to standard error of mean), where s is a letter. Different letters within a column indicate significant differences (p-value p < 0.05) based on a Wilcoxon signed rank test.
Objective Evaluation Details: Each model generates 16 samples for each of 6 test pieces. Negative Log Likelihood (NLL) is computed by inputing the molody and accompaniment into the MuseCoco 1B model.
Subjective Evaluation Details: One piece cherry-picked from 16 samples of each test piece, resulting in 6 pages of questions. We collect responses from 22 participants (18 complete submissions and 4 partial submissions). For complete submissions, the average completion time is 16min 59s.
