Denoising without access to clean data using a partitioned autoencoder release_rybmtwfelvfodnltayhveijcnm

by Dan Stowell, Richard E. Turner

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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.
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Type  article
Stage   submitted
Date   2015-09-20
Version   v1
Language   en ?
arXiv  1509.05982v1
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