IEEE Transactions on Emerging Topics in Computational Intelligence currently offers publication of its highlighted papers in Open Access for the duration of 3 months in order to assist authors gain maximum exposure for their groundbreaking research and application-oriented papers to all reader communities.
The second highlighted paper offered to make Open Access in TETCI is available now for the duration of 3 months starting from 1 April 2019.
We hope you will enjoy reading our second Open Access paper.
End-to-End Learning for Physics-Based Acoustic Modeling
Authors: Leonardo Gabrielli, Stefano Tomassetti, Carlo Zinato and Francesco Piazza
Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)
Issue: Volume 2, Issue 2 – April 2018
Abstract: 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.
Index Terms: Physics-based acoustic modeling, End-to-end learning, Convolutional neural networks
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8323323
Available in Open Access from 1 April 2019 to 30 June 2019 in IEEE Xplore Digital Library.