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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Pubblicato online: 03 feb 2025
Ricevuto: 27 set 2024
Accettato: 29 dic 2024
DOI: https://doi.org/10.2478/amns-2025-0036
Parole chiave
© 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 |