This repository contains a Tensorflow implementation of the VYAE autoencoder for interaction sound effects. Details and application examples can be found in the reference below and on the accompanying website.
Simon Schwär, Meinard Müller, and Sebastian J. Schlecht: A Variational Y-Autoencoder for Disentangling Gesture and Material of Interaction Sounds. In AES 4th International Conference on Audio for Virtual and Augmented Reality (AES AVAR) – submitted for peer review, Redmond, WA , USA, 2022.
Required software packages: tensorflow (tested with versions 2.6 and 2.7), numpy (tested with versions 1.19 and 1.21). The demonstration notebook further requires: jupyterlab, matplotlib, librosa
The repository contains the saved weights of the model trained with the Spoon/Bowl dataset. Please see the notebook explore_trained_model.ipynb for a usage example.
The file train_model.py can be used to train the model on other data. Some API documentation can be found in the vyae package.