Feature Analysis and Application of Music Works Based on Artificial Neural Network
oraz
27 lut 2025
O artykule
Data publikacji: 27 lut 2025
Otrzymano: 04 paź 2024
Przyjęty: 26 sty 2025
DOI: https://doi.org/10.2478/amns-2025-0130
Słowa kluczowe
© 2025 Yu Wang 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.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

characteristics
Feature number | Feature name | Characteristic dimension |
---|---|---|
1 | zero crossing rate | 1 |
2 | root mean square | 1 |
3 | spectral centroid | 1 |
4 | spectralroll-off frequency | 1 |
5 | spectral contrast | 7 |
6 | MFCC | 12 |
experimental results
structure | Rnet1 | Rnet2 | Rnet3 | Rnet4 | Rnet5 |
---|---|---|---|---|---|
Circulation layer (128) | ✓ | ✓ | ✓ | ✓ | ✓ |
Circulation layer (128) | ✓ | ✓ | ✓ | ✓ | |
Circulation layer (128) | ✓ | ✓ | ✓ | ||
Circulation layer (128) | ✓ | ✓ | |||
Circulation layer (128) | ✓ | ||||
Pooling | Last | Last | Last | Last | Last |
Full connection (256) | ReLU | ReLU | ReLU | ReLU | ReLU |
Full connection (256) | ReLU | ReLU | ReLU | ReLU | ReLU |
Full connection (10/6) | Softmax | Softmax | Softmax | Softmax | Softmax |
data set table
data set | GTAZN | ISMIR2022 |
---|---|---|
Rnetl | 78.32% | 78.41% |
Rnet2 | 79.67% | 79.53% |
Rnet3 | 80.14 | 81.22% |
Rnet4 | 79.09% | 81.30 |
Rnet5 | 77.73% | 78.05% |