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Deep convolutional neural networks overview - with music

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Keunwoo Choi

An introduction to CNNs

Engineering
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Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNN use-cases References Convolutional Neural Networks A brief explanation Keunwoo.Choi @qmul.ac.uk Centre for Digital Music, Queen Mary University of London, UK 1/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNN use-cases References 1 Overview CNNs vs DNNs CNN structures Inside CNNs 2 CNN use-cases Image Music 3 References 2/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References CNNs: Convolutional Neural Networks (Deep) Convolutional Neural Networks deep = cascaded convolutional = filters neural networks = things are learned 1 2 1 cns.org 2 AlexNet 3/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References CNNs vs. general DNNs DNNs: fully-connected 3 CNNs: locally-connected and shared 3 urlhttp://cs231n.github.io/convolutional-networks/ 4/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolution 45 Example: 200x200 image 40K hidden units ~2B parameters!!! - Spatial correlation is local - Waste of resources + we have not enough training samples anyway.. Fully Connected Layer Ranzato 5/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering 48 Convolutional Layer Share the same parameters across different locations (assuming input is stationary): Convolutions with learned kernels Ranzato 6/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolutional Layer Ranzato 7/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolutional Layer Ranzato 8/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolutional Layer Ranzato 9/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolutional Layer Ranzato 10/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolutional Layer Ranzato 11/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolutional Layer Ranzato 12/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolutional Layer Ranzato 13/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolutional Layer Ranzato 14/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolutional Layer Ranzato 15/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolutional Layer Ranzato 16/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolutional Layer Ranzato 17/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolutional Layer Ranzato 18/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolutional Layer Ranzato 19/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolutional Layer Ranzato 20/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolutional Layer Ranzato 21/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Convolutional Layer RanzatoMathieu et al. “Fast training of CNNs through FFTs” ICLR 2014 22/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Convolution == filtering Example: vertical edge detector Convolutional Layer * -1 0 1 -1 0 1 -1 0 1 Ranzato = 23/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References CNN structures Convolutional layers + something else 1 [6] Many convolutional layers that learn filters, and subsampling layers that reduce sizes and add invariances 24/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References CNN structures Convolutional layers + something else 2 [1] Many convolutional layers that learn filters, and subsampling layers that reduce sizes and add invariances 25/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References Hierarchical features Hierarchical feature learning Each layer learns features in different levels of hierarchy High-level features are built on low-level features E.g. Layer 1: Edges (low-level, concrete) Layer 2: Simple shapes Layer 3: Complex shapes Layer 4: More complex shapes Layer 5: Shapes of target objects (high-level, abstract) 26/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References What is learned in CNNs? in image recognition task [11] 27/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References What is learned in CNNs? in image recognition task [11] 28/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References What is learned in CNNs? in image recognition task [11] 29/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References What is learned in CNNs? 1/2 in music genre classification task Layer 1/5 Bach Original Dream Toy Eminem Bach [Feature 1-9], Crude onset detector Dream Toy Eminem Bach [Feature 1-27], Onset detector Dream Toy Eminem [2] blog demo 30/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References What is learned in CNNs? 1/2 in music genre classification task Layer 2/5 Bach Original Dream Toy Eminem Bach [Feature 2-0], Good onset detector Dream Toy Eminem Bach [Feature 2-1], Bass note selector Dream Toy Eminem Bach [Feature 2-10], Harmonic selector Dream Toy Eminem Bach [Feature 2-48], Melody (large energy) Dream Toy Eminem [2] 31/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References What is learned in CNNs? 1/2 in music genre classification task Layer 3/5 Bach Original Dream Toy Eminem Bach [Feature 3-1], Better onset detector Dream Toy Eminem Bach [Feature 3-7], Melody (top note) Dream Toy Eminem Bach [Feature 3-38], Kick drum extractor Dream Toy Eminem Bach [Feature 3-40], Percussive eraser Dream Toy Eminem [2] 32/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References What is learned in CNNs? 1/2 in music genre classification task Layer 4/5 Bach Original Dream Toy Eminem Bach [Feature 4-5], Lowest notes selector Dream Toy Eminem Bach [Feature 4-11], Vertical line eraser Dream Toy Eminem Bach [Feature 4-30], Long horizontal line selector Dream Toy Eminem [2] 33/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References What is learned in CNNs? 1/2 in music genre classification task Layer 5/5 Bach Original Dream Toy Eminem Bach [Feature 5-11], texture 1 Dream Toy Eminem Bach [Feature 5-15], texture 2 Dream Toy Eminem Bach [Feature 5-56], Harmo-Rhythmic structure Dream Toy Eminem Bach [Feature 5-33], texture 3 Dream Toy Eminem [2] 34/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNNs vs DNNs CNN structures Inside CNNs CNN use-cases References What is learned in CNNs? 2/2 in music tagging task: Learn the transform! Audio → 2-D representation [3] 35/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNN use-cases Image Music References CNN use-cases Visual image recognition 36/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNN use-cases Image Music References CNN use-cases Image segmentation [12] 37/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNN use-cases Image Music References CNN use-cases Artistic style [4] 38/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNN use-cases Image Music References CNN use-cases Music information retrieval Anything people can do by seeing spectrograms E.g. Auto tagging [1], chord recognition [5], instrument recognition [7], music-noise segmentation [8], onset detection [9], boundary detection [10] + style change? source separation? effects/de-effects? 39/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNN use-cases References References I Choi, K., Fazekas, G., Sandler, M.: Automatic tagging using deep convolutional neural networks. In: Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York, USA (2016) Choi, K., Fazekas, G., Sandler, M.: Explaining convolutional neural networks on music classification (submitted). In: IEEE Conference on Machine Learning and Signal Processing (2016) Dieleman, S., Schrauwen, B.: End-to-end learning for music audio. In: Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. pp. 6964–6968. IEEE (2014) 40/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNN use-cases References References II Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576 (2015) Humphrey, E.J., Bello, J.P.: From music audio to chord tablature: Teaching deep convolutional networks toplay guitar. In: Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. pp. 6974–6978. IEEE (2014) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Li, P., Qian, J., Wang, T.: Automatic instrument recognition in polyphonic music using convolutional neural networks. arXiv preprint arXiv:1511.05520 (2015) 41/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNN use-cases References References III Park, T., Lee, T.: Music-noise segmentation in spectrotemporal domain using convolutional neural networks. ISMIR late-breaking session (2015) Schluter, J., Bock, S.: Improved musical onset detection with convolutional neural networks. In: International Conference on Acoustics, Speech and Signal Processing. IEEE (2014) Ullrich, K., Schl¨uter, J., Grill, T.: Boundary detection in music structure analysis using convolutional neural networks. In: Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR 2014), Taipei, Taiwan (2014) 42/43
Convolutional Neural Networks Keunwoo.Choi @qmul.ac.uk Overview CNN use-cases References References IV Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Computer Vision–ECCV 2014, pp. 818–833. Springer (2014) Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.H.: Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1529–1537 (2015) 43/43

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