Guided Generative Adversarial Neural Network for Representation Learning and High Fidelity Audio Generation using Fewer Labelled Audio Data

Understand

Recent improvements in Generative Adversarial Neural Networks (GANs) have shown their ability to generate higher quality samples as well as to learn good representations for transfer learning.

• Most of the representation learning methods based on GANs learn representations ignoring their post-use scenario, which can lead to increased generalisation ability.
• However, the model can become redundant if it is intended for a specific task.
• For example, assume we have a vast unlabelled audio dataset, and we want to learn a representation from this dataset so that it can be used to improve the emotion recognition performance of a small labelled audio dataset.