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In this paper, we propose a source separation method that is trained byobserving the mixtures and the class labels of the sources present in themixture without any access to isolated sources. Since our method does notrequire source class labels for every time-frequency bin but only a singlelabel for each source constituting the mixture signal, we call this scenario asweak class supervision. We associate a variational autoencoder (VAE) with eachsource class within a non-negative (compositional) model. Each VAE provides aprior model to identify the signal from its associated class in a soundmixture. After training the model on mixtures, we obtain a generative model foreach source class and demonstrate our method on one-second mixtures ofutterances of digits from 0 to 9. We show that the separation performanceobtained by source class supervision is as good as the performance obtained bysource signal supervision.

Audio Source Separation Using Variational Autoencoders and Weak Class Supervision