  Long Short-Term Memory networks (LSTMs) can be trained to realize inverse
control of physics-based sound synthesizers. Physics-based sound synthesizers
simulate the laws of physics to produce output sound according to input gesture
signals. When a user's gestures are measured in real time, she or he can use
them to control physics-based sound synthesizers, thereby creating simulated
virtual instruments. An intriguing question is how to program a computer to
learn to play such physics-based models. This work demonstrates that LSTMs can
be trained to accomplish this inverse control task with four physics-based
sound synthesizers.
