Rishabh Mehrotra, Niannan Xue, Mounia Lalmas
Neural Music Synthesis for Flexible Timbre Control
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.
Rishabh Mehrotra, Ashish Gupta
Federico Tomasi, Praveen Chandar, Gal Levy-Fix, Mounia Lalmas, Zhenwen Dai