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Separating mixed distributions is a long standing challenge for machinelearning and signal processing. Most current methods either rely on makingstrong assumptions on the source distributions or rely on having trainingsamples of each source in the mixture. In this work, we introduce a newmethod---Neural Egg Separation---to tackle the scenario of extracting a signalfrom an unobserved distribution additively mixed with a signal from an observeddistribution. Our method iteratively learns to separate the known distributionfrom progressively finer estimates of the unknown distribution. In somesettings, Neural Egg Separation is initialization sensitive, we thereforeintroduce Latent Mixture Masking which ensures a good initialization. Extensiveexperiments on audio and image separation tasks show that our methodoutperforms current methods that use the same level of supervision, and oftenachieves similar performance to full supervision.

Neural separation of observed and unobserved distributions