A Study of Using Deep Learning Technology to Improve the Accuracy of Polyphonic Singing in Community Choirs
oraz
03 lut 2025
O artykule
Data publikacji: 03 lut 2025
Otrzymano: 27 wrz 2024
Przyjęty: 29 gru 2024
DOI: https://doi.org/10.2478/amns-2025-0036
Słowa kluczowe
© 2025 Yue Li et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Figure 1.
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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 |