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We present a single deep learning architecture that can both separate anaudio recording of a musical mixture into constituent single-instrumentrecordings and transcribe these instruments into a human-readable format at thesame time, learning a shared musical representation for both tasks. This novelarchitecture, which we call Cerberus, builds on the Chimera network for sourceseparation by adding a third "head" for transcription. By training each headwith different losses, we are able to jointly learn how to separate andtranscribe up to 5 instruments in our experiments with a single network. Weshow that the two tasks are highly complementary with one another and whenlearned jointly, lead to Cerberus networks that are better at both separationand transcription and generalize better to unseen mixtures.

Simultaneous Separation and Transcription of Mixtures with Multiple Polyphonic and Percussive Instruments