  We propose a new framework for single-channel source separation that lies
between the fully supervised and unsupervised setting. Instead of supervision,
we provide input features for each source signal and use convex methods to
estimate the correlations between these features and the unobserved signal
decomposition. We analyze the case of $\ell_2$ loss theoretically and show that
recovery of the signal components depends only on cross-correlation between
features for different signals, not on correlations between features for the
same signal. Contextually supervised source separation is a natural fit for
domains with large amounts of data but no explicit supervision; our motivating
application is energy disaggregation of hourly smart meter data (the separation
of whole-home power signals into different energy uses). Here we apply
contextual supervision to disaggregate the energy usage of thousands homes over
four years, a significantly larger scale than previously published efforts, and
demonstrate on synthetic data that our method outperforms the unsupervised
approach.
