UCLA

Fires, rainstorms or insect swarms produce natural sounds made up of rapidly occurring

acoustic events. which we call ”sound textures”. This kind of phenomena have been studied

by computational audio community [MS11] and neural science people for a long time. From

previous studies, it has been verified that sound textures can be schematically synthesized

from statistical models fairly well. Here we take a novel approach involving neural networks

or deep learning methods. Specifically, we use cooperative training of a descriptor and a

generator network, modeled as a convolutional neural network(ConvNet) and a deconvolu-

tional neural network(DeconvNet) respectively. From several experiments, we proved that

our framework can capture the essence of sound textures and synthesize identifiable natural

sound