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A Recurrent Neural Network (RNN) for audio synthesis is trained by augmentingthe audio input with information about signal characteristics such as pitch,amplitude, and instrument. The result after training is an audio synthesizerthat is played like a musical instrument with the desired musicalcharacteristics provided as continuous parametric control. The focus of thispaper is on conditioning data-driven synthesis models with real-valuedparameters, and in particular, on the ability of the system a) to generalizeand b) to be responsive to parameter values and sequences not seen duringtraining.

Real-valued parametric conditioning of an RNN for interactive sound synthesis