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Design of an Intelligent Teaching Platform under Multidimensional Data Fusion in Music Performance Teaching

Ni Li

Ni Li

  • Department of Music, College of Humanities and Social Sciences, Fuzhou UniversityFuzhou, China
  • Department of Humanities and Arts, College of Zhicheng, Fuzhou UniversityFuzhou, China
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Li, Ni
 und 
Youyu Xiao

Youyu Xiao

Department of Music, College of Humanities and Social Sciences, Fuzhou UniversityFuzhou, China
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Xiao, Youyu
  
26. März 2025
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Online veröffentlicht: 26. März 2025
Eingereicht: 08. Nov. 2024
Akzeptiert: 09. Feb. 2025
DOI: https://doi.org/10.2478/amns-2025-0807
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© 2025 Ni Li et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Flow chart of chord sequence matching algorithm
Flow chart of chord sequence matching algorithm

Figure 2.

Multi-track pattern matching diagram
Multi-track pattern matching diagram

Figure 3.

Reliability test Step 1 Test result data
Reliability test Step 1 Test result data

Figure 4.

Reliability test Step 2 Test result data
Reliability test Step 2 Test result data

Figure 5.

Reliability test Step 3 Test result data
Reliability test Step 3 Test result data

Figure 6.

Reliability test Step 4 Test result data
Reliability test Step 4 Test result data

Comparison of system comprehensive results

Training data Test Data (Song Title) System type Correct rate of chord arrangement (%)
Music files in 450 MIDI music libraries Vacation Traditional HMM 74.21
Chord sequence matching algorithm 83.92
Better Hurry Up Traditional HMM 72.75
Chord sequence matching algorithm 82.83
Cool Day Traditional HMM 69.10
Chord sequence matching algorithm 74.35
Holiday Time Traditional HMM 72.24
Chord sequence matching algorithm 83.79
Better Us Traditional HMM 68.22
Chord sequence matching algorithm 73.97

Analysis table of students’ satisfaction after experiment (N=15)

Very satisfied Satisfaction General satisfaction Dissatisfy Far from satisfied
Teacher’s teaching ability 45% 42% 13% 0% 0%
Self-directed learning ability 32% 61% 7% 0% 0%
Platform usage 25% 56% 19% 0% 0%
Holistic evaluation of teaching 33% 62% 5% 0% 0%
Knowledge and skill level 44% 48% 8% 0% 0%

Comparison of traditional PCP and improved PCP results

Training data Test Data (Song Title) System type Correct rate of chord arrangement (%)
Music files in 450 MIDI music libraries Vacation Improved PCP+HMM 79.30
Chord sequence matching algorithm 84.78
Better Hurry Up Improved PCP+HMM 76.39
Chord sequence matching algorithm 82.55
Cool Day Improved PCP+HMM 72.62
Chord sequence matching algorithm 75.43
Holiday Time Improved PCP+HMM 78.12
Chord sequence matching algorithm 84.67
Better Us Improved PCP+HMM 72.19
Chord sequence matching algorithm 74.54
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Englisch
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