  This paper proposes a novel framework for unsupervised audio source
separation using a deep autoencoder. The characteristics of unknown source
signals mixed in the mixed input is automatically by properly configured
autoencoders implemented by a network with many layers, and separated by
clustering the coefficient vectors in the code layer. By investigating the
weight vectors to the final target, representation layer, the primitive
components of the audio signals in the frequency domain are observed. By
clustering the activation coefficients in the code layer, the previously
unknown source signals are segregated. The original source sounds are then
separated and reconstructed by using code vectors which belong to different
clusters. The restored sounds are not perfect but yield promising results for
the possibility in the success of many practical applications.
