End-to-End Learning for Physics-Based Acoustic Modeling

In past years, physics-based acoustic modeling developed theoretically to the point of yielding accurate understanding and description of a large number of acoustic phenomena, such as those involved in sound generation. Numerical algorithms have been proposed that are able to simulate these phenomena in real time with an acceptable computational cost, indeed reaching the market with commercial products. Sound synthesis based on physical models could benefit greatly from automated methods that require less specific know-how and save the sound-designer valuable time. This paper introduces a novel approach to parameter estimation in physics-based sound synthesis that is general and obtains good results based on an end-to-end computational intelligence paradigm. The approach is presented in a formal way and application to a practical use case is reported. Methodological issues, such as dataset generation, are investigated.