Appropriate sound effects are an important aspect of immersive virtual experiences. Particularly in mixed reality scenarios it may be desirable to change the acoustic properties of a naturally occurring interaction sound (e.g., the sound of a metal spoon scraping a wooden bowl) to a sound matching the characteristics of the corresponding interaction in the virtual environment (e.g., using wooden tools in a porcelain bowl). In this paper, we adapt the concept of a Y-Autoencoder (YAE) to the domain of sound e?ect analysis and synthesis. The YAE model makes it possible to disentangle the gesture and material properties of sound e?ects with a weakly supervised training strategy where only an identifier label for the material in each training example is given. We show that such a model makes it possible to resynthesize sound e?ects after exchanging the material label of an encoded example and obtain perceptually meaningful synthesis results with relatively low computational e?ort. By introducing a variational regularization for the encoded gesture, as well as an adversarial loss, we can further use the model to generate new and varying sound e?ects with the material characteristics of the training data, while the analyzed audio signal can originate from interactions with unknown materials.
https://www.aes.org/e-lib/browse.cfm?elib=21853
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!
This paper costs $33 for non-members and is free for AES members and E-Library subscribers.
Learn more about the AES E-Library
Start a discussion about this paper!
