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