  Collecting sufficient amount of data that can represent various acoustic
environmental attributes is a critical problem for distributed acoustic machine
learning. Several audio data augmentation techniques have been introduced to
address this problem but they tend to remain in simple manipulation of existing
data and are insufficient to cover the variability of the environments. We
propose a method to extend a technique that has been used for transferring
acoustic style textures between audio data. The method transfers audio
signatures between environments for distributed acoustic data augmentation.
This paper devises metrics to evaluate the generated acoustic data, based on
classification accuracy and content preservation. A series of experiments were
conducted using UrbanSound8K dataset and the results show that the proposed
method generates better audio data with transferred environmental features
while preserving content features.
