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![Task 4. Music emotion prediction
arousal
valence
RNN SVM
0.541
0.704
RNN SVM
0.320
0.500
[56] 4777-dim features
using which classifier](https://image.slidesharecdn.com/transferlearningismir2017-171028132043/85/Transfer-learning-for-music-classification-and-regression-tasks-18-320.jpg?cb=1563382001)
![Task 6. Audio event classification
Yes No
0.714
0.790
In [43],
data aug?](https://image.slidesharecdn.com/transferlearningismir2017-171028132043/85/Transfer-learning-for-music-classification-and-regression-tasks-20-320.jpg?cb=1563382001)
Transfer learning for music classification and regression tasks
- 1. Transfer learning Keunwoo.Choi @qmul.ac.uk for musicclassification and regression tasks The 18th International Society of Music Information Retrieval Conference, Suzhou, China, 2017
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- 3. Transfer learning Keunwoo.Choi @qmul.ac.uk for musicclassification and regression tasks 1. INTRODUCTION
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- 5. Not every task 10 100 1000 10000 100000 1000000 MSDFMA Cal10K Ballroom GTZan emoMusic Jamendo
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- 7. Transfer learning Keunwoo.Choi @qmul.ac.uk for musicclassification and regression tasks 2. THE PROPOSED METHOD
- 8. Train a convnet 5-layer 32-channel/layer Rock Jazz Pop Metal chillout beautiful mellow malevocal female vocal guitar 80s 90s 00s ...
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- 10. Knowledge = convnetstructure + weights convnet structure + trained weights convnet structure + random weights
- 11. Baselines convnet structure +random weights (32 - 160 dims) m, σ of {MFCC + dMFCC + ddMFCC} (120 dims) +‘12345’ + all MFCCs (160+120=280 dims) Many other reported methods (usually larger dims + selection) Rhythm Timbre Chords ... 🌀 🌀 🌀 🌀 Spectral ***🌀 🌀 🌀
- 12. Transfer learning Keunwoo.Choi @qmul.ac.uk for musicclassification and regression tasks 3. EXPERIMENTS
- 13. The target tasks TaskDataset #clips Ballroom dance genre classification Extended ballroom 4,180 Genre classification Gtzan genre 1,000 Speech/music classification Gtzan speech/music 128 Emotion prediction EmoMusic 744 Vocal/non-vocal classification Jamendo 4,086 Audio event classification Urbansound8K 8,732
- 14. Configurations • 10-fold cross-validation •Support Vector Machines and grid search Linear basis kernel Radial basis kernel Bandwidths of radial kernel Penalty parameters
- 15. Task 1. Ballroomgenre classification
- 16. Task 2. Gtzanmusic genre classification Arabi Beniya Huang Alexnet 0.780.840.880.91 SVM Classifier + • Arabi: spectral + beat + chord features • Beniya: extensive stats of spectral feats • Huang: spectral + pitch • Alexnet: transferred from visual obj recognition (final layer only, 4096-dim)
- 17. Task 3. Gtzanspeech/music classification
- 18. Task 4. Musicemotion prediction arousal valence RNN SVM 0.541 0.704 RNN SVM 0.320 0.500 [56] 4777-dim features using which classifier
- 19. Task 5. Vocal/non-vocalclassification
- 20. Task 6. Audioevent classification Yes No 0.714 0.790 In [43], data aug?
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- 22. How musical isMFCCs? “To some extent.”
- 23. Convnet feature How musicalis MFCCs? • For musical tasks, adding MFCCs to convnet hurts • For the non-musical task, it was better! • ..which might mean... MFCC’s musical aspect MFCC’s non-musical aspect “to this extent.” “maybe no more than convnet feature”
- 24. BONUS: genre classification(FMA dataset) https://github.com/keunwoochoi/FMA_convnet_features 0 20 40 60 80 LR kNN SVCrbf DT AdaBoost MFCC M/cont/ce non-EN Convnet 140 dims 196 dims 518 dims 160 dims
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- 26. Transfer learning Keunwoo.Choi @qmul.ac.uk for musicclassification and regression tasks 4. CONCLUSIONS
- 27. Conclusions • Exhaustive stillefficient multi-layer convnet feature • Different layer, different meaning • Music tagging as a source task seems versatile • Codes/weights are out there!
- 28. Transfer learning Keunwoo.Choi @qmul.ac.uk for musicclassification and regression tasks György Fazekas, Mark Sandler, Kyunghyun Cho The 18th International Society of Music Information Retrieval Conference, Suzhou, China, 2017
- 29. Links My blog |A blog post on this | Paper! | Codes and weights @keunwoochoi