  Training a denoising autoencoder neural network requires access to truly
clean data, a requirement which is often impractical. To remedy this, we
introduce a method to train an autoencoder using only noisy data, having
examples with and without the signal class of interest. The autoencoder learns
a partitioned representation of signal and noise, learning to reconstruct each
separately. We illustrate the method by denoising birdsong audio (available
abundantly in uncontrolled noisy datasets) using a convolutional autoencoder.
