Sounderfeit: Cloning a Physical Model using a Conditional Adversarial Autoencoder

An adversarial autoencoder conditioned on known parameters of a physical modeling bowed string synthesizer is evaluated for use in parameter estimation and resynthesis tasks. Latent dimensions are provided to capture 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.

Sounderfeit: Cloning a Physical Model with Conditional Adversarial Autoencoders

An adversarial autoencoder conditioned on known parameters of a physical modeling bowed string synthesizer is evaluated for use in parameter estimation and resynthesis tasks. Latent dimensions are provided to capture 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.

The Modular Audio Recognition Framework (MARF) and its Applications: Scientific and Software Engineering Notes

MARF is an open-source research platform and a collection of voice/sound/speech/text and natural language processing (NLP) algorithms written in Java and arranged into a modular and extensible framework facilitating addition of new algorithms. MARF can run distributively over the network and may act as a library in applications or be used as a source for learning and extension. A few example applications are provided to show how to use the framework. There is an API reference in the Javadoc format as well as this set of accompanying notes with the detailed description of the architectural design, algorithms, and applications. MARF and its applications are released under a BSD-style license and is hosted at SourceForge.net. This document provides the details and the insight on the internals of MARF and some of the mentioned applications.