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Most music source separation systems require large collections of isolatedsources for training, which can be difficult to obtain. In this work, we usemusical scores, which are comparatively easy to obtain, as a weak label fortraining a source separation system. In contrast with previous score-informedseparation approaches, our system does not require isolated sources, and scoreis used only as a training target, not required for inference. Our modelconsists of a separator that outputs a time-frequency mask for each instrument,and a transcriptor that acts as a critic, providing both temporal and frequencysupervision to guide the learning of the separator. A harmonic mask constraintis introduced as another way of leveraging score information during training,and we propose two novel adversarial losses for additional fine-tuning of boththe transcriptor and the separator. Results demonstrate that using scoreinformation outperforms temporal weak-labels, and adversarial structures leadto further improvements in both separation and transcription performance.

Transcription Is All You Need: Learning to Separate Musical Mixtures with Score as Supervision