neural_network_control_improvisation

Experiments on using Generative Adversarial Networks (GAN) and nondeterministic finite automaton (NFA) for learning controllers and mining specifications.

Name		Name	Last commit message	Last commit date
Latest commit History 203 Commits
audio		audio
.gitignore		.gitignore
ConvertData.ipynb		ConvertData.ipynb
GenerateData.ipynb		GenerateData.ipynb
README.md		README.md
SpecMiningInterpolation.ipynb		SpecMiningInterpolation.ipynb
SpecMiningMajor.ipynb		SpecMiningMajor.ipynb
bayesian_parameter_optimization.py		bayesian_parameter_optimization.py
convert_btext.py		convert_btext.py
crgan_proll.py		crgan_proll.py
data_processing.py		data_processing.py
dcgan_proll.py		dcgan_proll.py
gan.py		gan.py
gan_prefix_tf.py		gan_prefix_tf.py
gan_tf.py		gan_tf.py
gan_tf_interp.py		gan_tf_interp.py
gan_tf_sampling.py		gan_tf_sampling.py
gimg2midi.py		gimg2midi.py
gimg2txt.py		gimg2txt.py
grid_search.py		grid_search.py
layers.py		layers.py
lsgan_text.py		lsgan_text.py
midi2npyproll.py		midi2npyproll.py
models.py		models.py
music_utils.py		music_utils.py
neural_networks.py		neural_networks.py
nnet_utils.py		nnet_utils.py
npyproll2midi.py		npyproll2midi.py
parameter_search.py		parameter_search.py
params.py		params.py
plot_batch.py		plot_batch.py
text_utils.py		text_utils.py
traverse_latent.py		traverse_latent.py
wcgan.gif		wcgan.gif
wcrgan_proll.py		wcrgan_proll.py

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