Feature Analysis and Application of Music Works Based on Artificial Neural Network
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27. Feb. 2025
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Online veröffentlicht: 27. Feb. 2025
Eingereicht: 04. Okt. 2024
Akzeptiert: 26. Jan. 2025
DOI: https://doi.org/10.2478/amns-2025-0130
Schlüsselwörter
© 2025 Yu Wang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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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% |