Augmented Granular Synthesis Method for GAN Latent Space with Redundancy Parameter

In this paper we introduce an augmented granular sound synthesis method for a GAN latent space exploration in audio domain. We use the AI-terity musical in- strument for sound generating events in which the neural network (NN) parameters are optimised and then the features are used as a basis to generate new sounds. The exploration of a latent space is realised by creating a latent space through the original features of the training data set and finding the corresponding audio feature of the vector points in this space. Our proposed sound synthesis method can achieve multiple audio generation and sound synthesising events simultaneously without interrupting the playback grains. To do that we introduce redundancy parameter that schedules additional buffer slots divided from a large buffer slot, allowing multiple latent space vector points to be used in granular synthesis, in GPU real-time. Our implementation demonstrates that augmented buffer schedule slots can be used as a feature for a sound synthesis method to explore GAN-latent sound synthesis of granular-musical events with multiple generated audio samples without interrupting the granular musical features of the synthesis method.