Efficiency of AI Technology Application in Music Education - A Perspective Based on Deep Learning Model DLMM
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17 mar 2025
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Publicado en línea: 17 mar 2025
Recibido: 29 oct 2024
Aceptado: 07 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0326
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© 2025 Jie Chang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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Comparison of different algorithms
| Algorithm | P/% | R/% | mAP@0.5/% | Model Parameters/Mb | Fps | Gflop |
|---|---|---|---|---|---|---|
| CRNN-Lite | 92.7 | 91.6 | 93.3 | 10.7 | 90.9 | 56.3 |
| CRNN-Lite+DLMM | 95.6 | 92.7 | 94.6 | 9.5 | 92 | 55.2 |
| CRNN-Lite+Mm-Sada | 69 | 51.2 | 53.8 | 3 | 173.8 | 10.4 |
| CRNN-Lite+Mla | 81.4 | 57.5 | 71 | 13.7 | 143.7 | 26.8 |
| CRNN-Lite+Pfts | 84 | 56.6 | 68.8 | 26.2 | 106.4 | 66.6 |
| CRNN-Lite+ |
61.5 | 44.4 | 50.1 | 4.7 | 192.7 | 12.3 |
| MIR | 73.4 | 53.3 | 60.9 | 14.8 | 160.1 | 44 |
| MIR + DLMM | 79 | 57.9 | 59.7 | 49.8 | 83.2 | 166.4 |
| MIR+Mm-Sada | 80.7 | 42.6 | 49.7 | 37.3 | 161.9 | 104.1 |
| MIR+Mla | 73.6 | 51.7 | 59.9 | 3.9 | 150.9 | 8.9 |
| MIR+Pfts | 84.3 | 57.7 | 67.2 | 11.8 | 175.5 | 27.3 |
| MIR+ |
86.5 | 61.2 | 68.8 | 26.1 | 92.5 | 80.8 |
| Polyphonic-Tromr | 68.3 | 54.6 | 60.4 | 8.9 | 171.7 | 11.5 |
| Polyphonic-Tromr+ DLMM | 76 | 61.3 | 71.5 | 15.7 | 148.2 | 26 |
| Polyphonic-Tromr+Mm-Sada | 84.7 | 63.3 | 72.2 | 18.3 | 97.6 | 65.5 |
| Polyphonic-Tromr+Mla | 62.5 | 40.6 | 56.1 | 3.6 | 190.7 | 4.8 |
| Polyphonic-Tromr+Pfts | 70.5 | 54.7 | 60.1 | 14.5 | 161.1 | 41.3 |
| Polyphonic-Tromr+ |
80.8 | 57.4 | 59.6 | 55.7 | 77.6 | 162.2 |
Matched sample
| Pair Difference | T | Freedom | Significance (Double Tail) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Mean Value | Standard Deviation | Standard Error Mean | The Difference Is 95% Confidence Interval | ||||||
| Lower Limit | Upper Limit | ||||||||
| Pair 1 | Study Interest- Study Interest 2 | -1.95025 | 0.92366 | 0.12985 | -2.23115 | -1.68512 | -15.122 | 50 | 0.000 |
| Pair 2 | Learning Motivation- Learning Motivation 2 | -2.01585 | 0.80051 | 0.11362 | -2.24985 | -1.77544 | -17.854 | 50 | 0.000 |
| Pair 3 | Learning Efficiency- Learning Efficiency 2 | -2.10305 | 0.78442 | 0.10885 | -2.33165 | -1.87322 | -19.305 | 50 | 0.000 |
| Pair 4 | Learning Emotion- Learning Emotion 2 | -2.11065 | 0.76051 | 0.10601 | -2.42112 | -1.87622 | -20.055 | 50 | 0.000 |
Match sample analysis
| Mean Value | N | Standard Deviation | Standard Error Mean | ||
|---|---|---|---|---|---|
| Pair 1 | Study Interest | 2.3891 | 50 | 0.69622 | 0.09855 |
| Study Interest 2 | 4.3395 | 50 | 0.55987 | 0.06577 | |
| Pair 2 | Learning Motivation | 2.2501 | 50 | 0.53612 | 0.07602 |
| Learning Motivation 2 | 4.2651 | 50 | 0.52584 | 0.07362 | |
| Pair 3 | Learning Efficiency | 2.0561 | 50 | 0.49532 | 0.6855 |
| Learning Efficiency 2 | 4.1658 | 50 | 0.57846 | 0.08107 | |
| Pair 4 | Learning Emotion | 2.2989 | 50 | 0.59336 | 0.08425 |
| Learning Emotion 2 | 4.4102 | 50 | 0.51322 | 0.07321 |
Experiment length of different algorithms
| Algorithm | Training time(s) | Test time(s) |
|---|---|---|
| CRNN-Lite | 2215 | 465 |
| CRNN-Lite+DLMM | 1985 | 412 |
| CRNN-Lite+Mm-Sada | 3265 | 802 |
| CRNN-Lite+Mla | 2657 | 495 |
| CRNN-Lite+Pfts | 3019 | 777 |
| CRNN-Lite+ |
3530 | 778 |
| MIR | 2906 | 555 |
| MIR + DLMM | 4263 | 1011 |
| MIR+Mm-Sada | 2981 | 693 |
| MIR+Mla | 3265 | 780 |
| MIR+Pfts | 2648 | 504 |
| MIR+ |
2999 | 782 |
| Polyphonic-Tromr | 3523 | 770 |
| Polyphonic-Tromr+ DLMM | 2896 | 534 |
| Polyphonic-Tromr+Mm-Sada | 4260 | 1007 |
| Polyphonic-Tromr+Mla | 2969 | 683 |
| Polyphonic-Tromr+Pfts | 2983 | 796 |
| Polyphonic-Tromr+ |
3515 | 797 |
Student song performance
| Class | Laboratory Class(N=50) | Cross-Reference Class(N=50) | ||
|---|---|---|---|---|
| Number | Percentage | Number | Percentage | |
| ① | 33 | 66% | 20 | 40% |
| ② | 15 | 30% | 18 | 36% |
| ③ | 1 | 2% | 8 | 16% |
| ④ | 1 | 2% | 4 | 8% |