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An adversarial autoencoder conditioned on known parameters of a physicalmodeling bowed string synthesizer is evaluated for use in parameter estimationand resynthesis tasks. Latent dimensions are provided to capture variance notexplained by the conditional parameters. Results are compared with and withoutthe adversarial training, and a system capable of "copying" a givenparameter-signal bidirectional relationship is examined. A real-time synthesissystem built on a generative, conditioned and regularized neural network ispresented, allowing to construct engaging sound synthesizers based purely onrecorded data.

Sounderfeit: Cloning a Physical Model using a Conditional Adversarial Autoencoder