I’ve a weakness for ideas that give me plausible deniability for making generative art while doing my maths homework.
Quasimondo: so do you
This page is more chaotic than the already-chaotic median, sorry. Good luck making sense of it.
See also analysis/resynthesis.
See gesture recognition. Oh and also google’s AMI channel, and ml4artists, which has some sweet machine learning for artists topic guides.
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.
There are NIPS streams about this now.
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.
See those classic images from google’s tripped-out image recognition systems) or Gatys, Ecker and Bethge’s deep art Neural networks do a passable undergraduate Monet.
Here’s Frank Liu’s implementation of style transfer in pycaffe.
Alex Graves, Generating Sequences With Recurrent Neural Networks, generates handwriting. Relatedly, sketch-rnn is reaaaally cute.
Deep dreaming approaches are entertaining. (NSFW) Here’s a more pedestrian and slightly more informative version of that.
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.
Google has weighed in, like a gorilla on the metallophone, to do midi composition with Tensorflow as part of their Magenta project. Their NIPS 2016 demo won the best demo prize.
There is some useful infrastructure:
pypianoroll for piano rolls music21 formidi scores respective and MIDO for live midi.
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.
Ji-Sung Kim’s deepjazz project is minimal, but does interesting jazz improvisations. Part of the genius here is choosing totally chaotic music to try to ape, so you can ape it chaotically. (Code)
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.
A tutorial on generating music using Restricted Boltzmann Machines for the conditional random field density, and an RNN for the time dependence after (Boulanger-Lewandowski, Bengio, and Vincent 2012)
Bob Sturm did a good one
🚧 google’s latest demo in this area was popular. Deep Bach code) seems to be doing a related thing. Similar sets of authors have some other related work):
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.
See analysis/resynthesis, voice fakes.
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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.
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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.
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