  Evolutionary Algorithms and Generative Deep Learning have been two of the
most powerful tools for sound generation tasks. However, they have limitations:
Evolutionary Algorithms require complicated designs, posing challenges in
control and achieving realistic sound generation. Generative Deep Learning
models often copy from the dataset and lack creativity. In this paper, we
propose LVNS-RAVE, a method to combine Evolutionary Algorithms and Generative
Deep Learning to produce realistic and novel sounds. We use the RAVE model as
the sound generator and the VGGish model as a novelty evaluator in the Latent
Vector Novelty Search (LVNS) algorithm. The reported experiments show that the
method can successfully generate diversified, novel audio samples under
different mutation setups using different pre-trained RAVE models. The
characteristics of the generation process can be easily controlled with the
mutation parameters. The proposed algorithm can be a creative tool for sound
artists and musicians.
