diff README.md @ 0:73317239d6d1

autoencoder-specgram first checkin

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+
+Spectrogram auto-encoder
+(c) Dan Stowell 2016.
+
+
+A simple example of an autoencoder set up for spectrograms, with two convolutional layers - thought of as one "encoding" layer and one "decoding" layer.
+
+It's meant to be a fairly minimal example of doing this in Theano, using the Lasagne framework to make things easier.
+
+By default it simply makes a training set from different chunks of the same single spectrogram (from the supplied wave file). This is not a good training set!
+
+Notable (potentially unusual) things about this implementation:
+ * Data is not pre-whitened, instead we use a custom layer (NormalisationLayer) to normalise the mean-and-variance of the data for us. This is because I want the spectrogram to be normalised when it is input but not normalised when it is output.
+ * It's a convolutional net but only along the time axis; along the frequency axis it's fully-connected.
+ * There's no maxpooling/downsampling.
+
+
+SYSTEM REQUIREMENTS
+===================
+
+* Python
+* Theano (NOTE: please check the Lasagne page for preferred Theano version)
+* Lasagne https://github.com/Lasagne/Lasagne
+* Matplotlib
+* scikits.audiolab
+
+Tested on Ubuntu 14.04 with Python 2.7.
+
+USAGE
+=====
+
+          python autoencoder-specgram.py
+
+It creates a "pdf" folder and puts plots in there (multi-page PDFs) as it goes along.
+There's a "progress" pdf which gets repeatedly overwritten - you should see the output quality gradually getting better.
+
+Look in userconfig.py for configuration options.
+