Daniel Hasenklever

Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation

We propose a training scheme to train neural network-based source separation algorithms from scratch when parallel clean data is unavailable. In particular, we demonstrate that an unsupervised spatial clustering algorithm is sufficient to guide the training of a deep clustering system. We argue that…