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.

该论文提出了一种利用深度自编码器进行无监督音频源分离的新框架，在适当配置的自编码器的帮助下，通过对编码器向量进行聚类，自动分离混合输入中未知源信号的特征。通过调查权重向量并对激活系数在代码层进行聚类，观察了频域中音频信号的原始组件。通过使用属于不同聚类的代码向量，分离并重构原始源声音。虽然回复的音频不是完美的，但在许多实际应用中可能会产生有希望的结果。