I’ve a weakness for ideas that give me plausible deniability for making generative art while doing my maths homework.
This page is more chaotic than the already-chaotic median, sorry. Good luck making sense of it.
See also analysis/resynthesis.
Many neural networks, are generative in the sense that even if you train ’em to classify things, they can also predict new members of the class. e.g. run the model forwards, it recognizes melodies; run it “backwards”, it composes melodies. Or rather, you maybe trained them to generate examples in the course of training them to detect examples.
There are many definitional and practical wrinkles here, and this quality is not unique to artificial neural networks, but it is a great convenience, and the gods of machine learning have blessed us with much infrastructure to exploit this feature, because it is very close to actual profitable algorithms. Upshot: There is now a lot of computation and grad student labour directed at producing neural networks which as a byproduct can produce faces, chairs, film dialogue, symphonies and so on.
Some as-yet-unfiled neural-artwork links I should think about.
- So simple it’s cute, CPPNs are probably what Jonathan McCabe has been producing for years.
- IGAN, iGAN: Interactive Image Generation via Generative Adversarial Networks
- interpolating style transfer.
- neurogram is a compact semi-untrained neural network image synthesis-in-the-browser
- Adversarial generation is a cool hack if you hate boring stuff like labelling data sets e.g. chair generation
- Autoencoding beyond pixels using a learned similarity metric (Larsen et al. 2015) code The clever hack here is the “generative adversarial networks”
- Ross Gibson Adventures in narrated reality gives an overview of text generation using RNNs.
Alex Graves, Generating Sequences With Recurrent Neural Networks, generates handwriting. Relatedly, sketch-rnn is reaaaally cute.
Distill.pub has some lovely visual explanations of visual and other neural networks:
Experiments in Handwriting with a Neural Network
Deconvolution and Checkerboard Artifacts
How to Use t-SNE Effectively
Attention and Augmented Recurrent Neural Networks
hardmaru presents an amazing introduction to running sophisticated neural networks in the browser, targeted at artists, which goes over the handwriting post in a non-technical way.
a platform for creators of all kinds to use machine learning tools in intuitive ways without any coding experience. Find resources here to start creating with RunwayML quickly.
In particular it plugs into Blender and Photoshop and allows you to use those programs as a UI for ML-backed algorithms. Nice.
Symbolic composition via scores/MIDI/etc
Seems like it should be easy, until you think about it.
Related: Arpeggiate by numbers which discusses music-theory.
There is some useful infrastructure:
Daniel Johnson has a convolutional and recurrent architecture for taking into account multiple types of dependency in music, which he calls biaxial neural network Zhe LI, Composing Music With Recurrent Neural Networks.
Boulanger-Lewandowski, (code and data) for (Boulanger-Lewandowski, Bengio, and Vincent 2012)’s recurrent neural network composition using python/Theano. Christian Walder leads a project which shares some roots with that. (Walder 2016a, 2016b) Bob Sturm’s FolkRNN does a related thing, but ingeniously redefines the problem by focussing on folk tune notation.
Modeling polyphonic music is a particularly challenging task because of the intricate interplay between melody and harmony. A good model should satisfy three requirements: statistical accuracy (capturing faithfully the statistics of correlations at various ranges, horizontally and vertically), flexibility (coping with arbitrary user constraints), and generalization capacity (inventing new material, while staying in the style of the training corpus). Models proposed so far fail on at least one of these requirements. We propose a statistical model of polyphonic music, based on the maximum entropy principle. This model is able to learn and reproduce pairwise statistics between neighboring note events in a given corpus. The model is also able to invent new chords and to harmonize unknown melodies. We evaluate the invention capacity of the model by assessing the amount of cited, re-discovered, and invented chords on a corpus of Bach chorales. We discuss how the model enables the user to specify and enforce user-defined constraints, which makes it useful for style-based, interactive music generation.
Evan Chow represents for team non-deep-learning with jazzml:
Computer jazz improvisation powered by machine learning, specifically trigram modeling, K-Means clustering, and chord inference with SVMs.
Charles Martin’s Creative Predictions:
Creative Prediction is about applying predictive machine learning models to creative data. The focus is on recurrent neural networks (RNNs), deep learning models that can be used to generate sequential and temporal data. RNNs can be applied to many kinds of creative data including text and music. They can learn the long-range structure from a corpus of data and “create” new sequences by predicting one element at a time. When embedded in a creative interface, they can be used for “predictive interaction” where a human collaborates with, influences, and is influenced by a generative neural network.
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Bown, Oliver, and Sebastian Lexer. 2006. “Continuous-Time Recurrent Neural Networks for Generative and Interactive Musical Performance.” In Applications of Evolutionary Computing, edited by Franz Rothlauf, Jürgen Branke, Stefano Cagnoni, Ernesto Costa, Carlos Cotta, Rolf Drechsler, Evelyne Lutton, et al., 652–63. Lecture Notes in Computer Science 3907. Springer Berlin Heidelberg. http://link.springer.com/chapter/10.1007/11732242_62.
Champandard, Alex J. 2016. “Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks,” March. http://arxiv.org/abs/1603.01768.
Denton, Emily, Soumith Chintala, Arthur Szlam, and Rob Fergus. 2015. “Deep Generative Image Models Using a Laplacian Pyramid of Adversarial Networks,” June. http://arxiv.org/abs/1506.05751.
Dieleman, Sander, and Benjamin Schrauwen. 2014. “End to End Learning for Music Audio.” In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6964–8. IEEE. https://doi.org/10.1109/ICASSP.2014.6854950.
Dosovitskiy, Alexey, Jost Tobias Springenberg, Maxim Tatarchenko, and Thomas Brox. 2014. “Learning to Generate Chairs, Tables and Cars with Convolutional Networks,” November. http://arxiv.org/abs/1411.5928.
Dumoulin, Vincent, Jonathon Shlens, and Manjunath Kudlur. 2016. “A Learned Representation for Artistic Style,” October. http://arxiv.org/abs/1610.07629.
Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. 2015. “A Neural Algorithm of Artistic Style,” August. http://arxiv.org/abs/1508.06576.
Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. 2014. “Explaining and Harnessing Adversarial Examples,” December. http://arxiv.org/abs/1412.6572.
Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. “Generative Adversarial Nets.” In Advances in Neural Information Processing Systems 27, edited by Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, 2672–80. NIPS’14. Cambridge, MA, USA: Curran Associates, Inc. http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf.
Gregor, Karol, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, and Daan Wierstra. 2015. “DRAW: A Recurrent Neural Network for Image Generation,” February. http://arxiv.org/abs/1502.04623.
Gregor, Karol, and Yann LeCun. 2010. “Learning Fast Approximations of Sparse Coding.” In Proceedings of the 27th International Conference on Machine Learning (ICML-10), 399–406. http://machinelearning.wustl.edu/mlpapers/paper_files/icml2010_GregorL10.pdf.
———. 2011. “Efficient Learning of Sparse Invariant Representations,” May. http://arxiv.org/abs/1105.5307.
Grosse, Roger, Ruslan R. Salakhutdinov, William T. Freeman, and Joshua B. Tenenbaum. 2012. “Exploiting Compositionality to Explore a Large Space of Model Structures.” In Proceedings of the Conference on Uncertainty in Artificial Intelligence. http://arxiv.org/abs/1210.4856.
He, Kun, Yan Wang, and John Hopcroft. 2016. “A Powerful Generative Model Using Random Weights for the Deep Image Representation.” In Advances in Neural Information Processing Systems. http://arxiv.org/abs/1606.04801.
Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. 2006. “Reducing the Dimensionality of Data with Neural Networks.” Science 313 (5786): 504–7. https://doi.org/10.1126/science.1127647.
Jetchev, Nikolay, Urs Bergmann, and Roland Vollgraf. 2016. “Texture Synthesis with Spatial Generative Adversarial Networks.” In Advances in Neural Information Processing Systems 29. http://arxiv.org/abs/1611.08207.
Jing, Yongcheng, Yezhou Yang, Zunlei Feng, Jingwen Ye, and Mingli Song. 2017. “Neural Style Transfer: A Review,” May. http://arxiv.org/abs/1705.04058.
Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. 2016. “Perceptual Losses for Real-Time Style Transfer and Super-Resolution,” March. http://arxiv.org/abs/1603.08155.
Karras, Tero, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2017. “Progressive Growing of GANs for Improved Quality, Stability, and Variation.” In Proceedings of ICLR. http://arxiv.org/abs/1710.10196.
Karras, Tero, Samuli Laine, and Timo Aila. 2018. “A Style-Based Generator Architecture for Generative Adversarial Networks,” December. http://arxiv.org/abs/1812.04948.
Larsen, Anders Boesen Lindbo, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther. 2015. “Autoencoding Beyond Pixels Using a Learned Similarity Metric,” December. http://arxiv.org/abs/1512.09300.
Lazaridou, Angeliki, Dat Tien Nguyen, Raffaella Bernardi, and Marco Baroni. 2015. “Unveiling the Dreams of Word Embeddings: Towards Language-Driven Image Generation,” June. http://arxiv.org/abs/1506.03500.
Li, Yanghao, Naiyan Wang, Jiaying Liu, and Xiaodi Hou. 2017. “Demystifying Neural Style Transfer.” In IJCAI. http://arxiv.org/abs/1701.01036.
Luo, Yi, Zhuo Chen, John R. Hershey, Jonathan Le Roux, and Nima Mesgarani. 2016. “Deep Clustering and Conventional Networks for Music Separation: Stronger Together,” November. http://arxiv.org/abs/1611.06265.
Malmi, Eric, Pyry Takala, Hannu Toivonen, Tapani Raiko, and Aristides Gionis. 2016. “DopeLearning: A Computational Approach to Rap Lyrics Generation,” 195–204. https://doi.org/10.1145/2939672.2939679.
Mital, Parag K. 2017. “Time Domain Neural Audio Style Transfer,” November. http://arxiv.org/abs/1711.11160.
Mnih, Andriy, and Karol Gregor. 2014. “Neural Variational Inference and Learning in Belief Networks.” In Proceedings of the 31st International Conference on Machine Learning. http://www.jmlr.org/proceedings/papers/v32/mnih14.html.
Olah, Chris, Alexander Mordvintsev, and Ludwig Schubert. 2017. “Feature Visualization.” Distill 2 (11): e7. https://doi.org/10.23915/distill.00007.
Oord, Aäron van den. 2016. “Wavenet: A Generative Model for Raw Audio.”
Oord, Aäron van den, Nal Kalchbrenner, and Koray Kavukcuoglu. 2016. “Pixel Recurrent Neural Networks,” January. http://arxiv.org/abs/1601.06759.
Oord, Aäron van den, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu. 2016. “Conditional Image Generation with PixelCNN Decoders,” June. http://arxiv.org/abs/1606.05328.
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Sturm, Bob L., Oded Ben-Tal, Úna Monaghan, Nick Collins, Dorien Herremans, Elaine Chew, Gaëtan Hadjeres, Emmanuel Deruty, and François Pachet. 2018. “Machine Learning Research That Matters for Music Creation: A Case Study.” Journal of New Music Research 0 (0): 1–20. https://doi.org/10.1080/09298215.2018.1515233.
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———. 2016b. “Symbolic Music Data Version 1.0,” June. http://arxiv.org/abs/1606.02542.
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