Pre-trained GAN generator weights

Pre-trained GAN Generator Weights for Inference

This release availability of pre-trained weights for the Generative Adversarial Network (GAN) generator, as described in the publication titled "Generative adversarial networks with physical sound field priors" by Xenofon Karakonstantis and Efren Fernandez-Grande. These weights have been trained on a large dataset, and we are now providing them to the community for inference purposes.

GAN Generator Overview:
The GAN generator is a deep neural network architecture designed to generate synthetic data that closely resembles the distribution of real data. This pre-trained GAN generator has been trained to achieve diverse sound fields in accordance with the methods presented in the publication.

• The generator model is implemented in TensorFlow, making it compatible with a wide range of deep learning frameworks.

Usage Instructions:
You can easily utilise the pre-trained GAN generator for sound field inference. Below are the steps to get started:

1. Install the required dependencies, including TensorFlow [2.8].

2. Download the pre-trained generator weights and unzip file

3. Load the weights into your TensorFlow-based project using the provided code snippet:

   import tensorflow as tf
   
   # Define your GAN generator architecture (ensure it matches the architecture used during training)
   generator = YourGANGenerator()
   
   # Load pre-trained weights
   generator.load_weights("path/to/pretrained_weights")
   
   # Generate synthetic sound fields using the generator
   synthetic_soundfield = generator(noise_input)