  The recent success of raw audio waveform synthesis models like WaveNet
motivates a new approach for music synthesis, in which the entire process ---
creating audio samples from a score and instrument information --- is modeled
using generative neural networks. This paper describes a neural music synthesis
model with flexible timbre controls, which consists of a recurrent neural
network conditioned on a learned instrument embedding followed by a WaveNet
vocoder. The learned embedding space successfully captures the diverse
variations in timbres within a large dataset and enables timbre control and
morphing by interpolating between instruments in the embedding space. The
synthesis quality is evaluated both numerically and perceptually, and an
interactive web demo is presented.
