Synthesis of plucked-string tones by physical modeling with recurrent neural networks

Physical modeling music synthesis techniques by simulating the vibrations of acoustic instruments are becoming more and more popular in the recent years. In the past few years, trying to find those appropriate parameters in order to make the synthesis result sound close to a specific instrument has been a very difficult problem. Various try-and-error methods based on frequency domain and time domain approaches were used mostly. In this paper, we propose a systematic analysis/synthesis model called the Scattering Recurrent Network (SRN) which is capable of modeling the dynamics of a plucked string and synthesizing tones which are very close to the tones produced by the same string. The sound quality is superior to both FM and Wavetable synthesis techniques