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.

中文翻译：

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分布式系统的环境转移

收集足以表示各种声学环境属性的数据量是分布式声学机器学习的关键问题。已经引入了几种音频数据增强技术来解决该问题，但是它们倾向于保留在对现有数据的简单操纵中，并且不足以覆盖环境的可变性。我们提出了一种扩展技术的方法，该技术已用于在音频数据之间传递声学样式纹理。该方法在环境之间传递音频签名，以进行分布式声学数据增强。本文基于分类的准确性和内容的保存，设计了评估生成的声学数据的指标。