Feature Analysis and Application of Music Works Based on Artificial Neural Network
y
27 feb 2025
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Publicado en línea: 27 feb 2025
Recibido: 04 oct 2024
Aceptado: 26 ene 2025
DOI: https://doi.org/10.2478/amns-2025-0130
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© 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% |