A real‐time music synthesis engine for physical modeling of plucked string instruments.

2009 ◽  
Vol 125 (4) ◽  
pp. 2684-2684
Author(s):  
Huynh V. Luong ◽  
Sangjin Cho ◽  
Jong‐Myon Kim ◽  
Uipil Chong
2018 ◽  
Vol 18 (1) ◽  
pp. 44-60 ◽  
Author(s):  
Stephen Sinclair

An adversarial autoencoder conditioned on known parameters of a physical modeling bowed string syn- thesizer is evaluated for use in parameter estimation and resynthesis tasks. Latent dimensions are provided to cap- ture variance not explained by the conditional parameters. Results are compared with and without the adversarial training, and a system capable of “copying” a given parameter-signal bidirectional relationship is examined. A real- -time synthesis system built on a generative, conditioned and regularized neural network is presented, allowing to construct engaging sound synthesizers based purely on recorded data. 


Author(s):  
Frank Taylor ◽  
S. Jayaram ◽  
U. Jayaram ◽  
Tatsuki Mitsui

Abstract This paper describes a methodology for simulating the virtual assembly of heavy machinery. Heavy machinery or parts are described in this paper as objects too heavy to safely lift with two hands. Virtual assembly of heavy machinery poses special problems that are not seen in assemblies composed of parts easily manipulated with human hands. This paper identifies some of the difficulties associated with real-time virtual assembly of heavy machinery, and proposes methods for addressing these problems. We describe a method for reorganizing the assembly tree outside of traditional CAD systems to better simulate assemblies with numerous parts. This allows the user to control the assembly sequences, which are simulated in the virtual environment without changing the assembly hierarchy of the original CAD model. This paper also proposes methods for simulation of overhead cranes and the physical modeling of crane-part interactions, providing real-time virtual manipulation of heavy objects.


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