A Study of Using Deep Learning Technology to Improve the Accuracy of Polyphonic Singing in Community Choirs
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03. Feb. 2025
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Online veröffentlicht: 03. Feb. 2025
Eingereicht: 27. Sept. 2024
Akzeptiert: 29. Dez. 2024
DOI: https://doi.org/10.2478/amns-2025-0036
Schlüsselwörter
© 2025 Yue Li et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Figure 1.
Figure 2.
Figure 3.
Figure 4.
Matched sample statistics
| Dimension | Mean | SD | N | ||
|---|---|---|---|---|---|
| Pair 1 | Pretest | Singing ability | 2.84 | 0.536 | 20 |
| Posttest | 3.66 | 0.475 | 20 | ||
| Pair 2 | Pretest | Breath control | 2.34 | 0.526 | 20 |
| Posttest | 4.16 | 0.511 | 20 | ||
| Pair 3 | Pretest | Timeliness | 2.33 | 0.475 | 20 |
| Posttest | 3.87 | 0.463 | 20 | ||
| Pair 4 | Pretest | Rhythm | 2.96 | 0.369 | 20 |
| Posttest | 4.08 | 0.529 | 20 | ||
| Pair 5 | Pretest | Chorus ability | 2.63 | 0.511 | 20 |
| Posttest | 3.87 | 0.414 | 20 | ||
| Pair 6 | Pretest | Expressiveness | 2.76 | 0.332 | 20 |
| Posttest | 4.11 | 0.442 | 20 | ||
Matched sample
| Pair difference | 95% confidence interval | t | df | Sig.(2-tail) | ||||
|---|---|---|---|---|---|---|---|---|
| Mean | Standard error mean | Lower limit | Upper limit | |||||
| Pair 1 | Singing ability | -0.82 | 0.236 | 0.756 | 1.236 | 3.641 | 8 | 0.000 |
| Pair 2 | Breath control | -1.82 | 0.254 | 1.423 | 2.341 | 6.324 | 8 | 0.001 |
| Pair 3 | Timeliness | -1.54 | 0.187 | 0.789 | 1.254 | 3.214 | 8 | 0.000 |
| Pair 4 | Rhythm | -1.12 | 0.236 | 0.741 | 1.274 | 5.321 | 8 | 0.003 |
| Pair 5 | Chorus ability | -1.24 | 0.255 | 0.755 | 1.149 | 5.412 | 8 | 0.002 |
| Pair 6 | Expressiveness | -1.35 | 0.214 | 0.763 | 1.254 | 6.102 | 8 | 0.000 |