  A recurrent Neural Network (RNN) is trained to predict sound samples based on
audio input augmented by control parameter information for pitch, volume, and
instrument identification. During the generative phase following training,
audio input is taken from the output of the previous time step, and the
parameters are externally controlled allowing the network to be played as a
musical instrument. Building on an architecture developed in previous work, we
focus on the learning and synthesis of transients - the temporal response of
the network during the short time (tens of milliseconds) following the onset
and offset of a control signal. We find that the network learns the particular
transient characteristics of two different synthetic instruments, and
furthermore shows some ability to interpolate between the characteristics of
the instruments used in training in response to novel parameter settings. We
also study the behaviour of the units in hidden layers of the RNN using various
visualisation techniques and find a variety of volume-specific response
characteristics.
