  Audio source separation is the process of separating a mixture (e.g. a pop
band recording) into isolated sounds from individual sources (e.g. just the
lead vocals). Deep learning models are the state-of-the-art in source
separation, given that the mixture to be separated is similar to the mixtures
the deep model was trained on. This requires the end user to know enough about
each model's training to select the correct model for a given audio mixture. In
this work, we automate selection of the appropriate model for an audio mixture.
We present a confidence measure that does not require ground truth to estimate
separation quality, given a deep model and audio mixture. We use this
confidence measure to automatically select the model output with the best
predicted separation quality. We compare our confidence-based ensemble approach
to using individual models with no selection, to an oracle that always selects
the best model and to a random model selector. Results show our
confidence-based ensemble significantly outperforms the random ensemble over
general mixtures and approaches oracle performance for music mixtures.
