Registration for ICASSP is free of charge, but registration is required to view the videos. If you have not yet registered, please visit: https://cmsworkshops.com/ICASSP2020/Registration.asp.Access the full virtual conference by visiting: https://2020.ieeeicassp-virtual.org/attendee/login. Your username is your email address and your password is your confirmation number/registration ID.
We present a single deep learning architecture that can both separate an audio recording of a musical mixture into constituent single-instrument recordings and transcribe these instruments into a human-readable format at the same time, learning a shared musical representation for both tasks. This novel architecture, which we call Cerberus, builds on the Chimera network for source separation by adding a third “head” for transcription. By training each head with different losses, we are able to jointly learn how to separate and tran- scribe up to five instruments with a single network. We show that separation and transcription are highly complementary with one another and when learned jointly, lead to Cerberus networks that are better at both separation and transcription and generalize better to unseen mixtures.