It is well-known that audio classifiers often rely on non-musically relevant features and spurious correlations to classify audio. Hence audio classifiers are easy to manipulate or confuse, resulting in wrong classifications. While inducing a misclassification is not hard, until now the set of features that the classifiers rely on was not well understood. In this paper we introduce a new method that uses causal reasoning to discover features of the frequency space that are sufficient and necessary for a given classification. We describe an implementation of this algorithm in the tool FreqReX and provide experimental results on a number of standard benchmark datasets. Our experiments show that causally sufficient and necessary subsets allow us to manipulate the outputs of the models in a variety of ways by changing the input very slightly. Namely, a change to one out of 240,000 frequencies results in a change in classification 58% of the time, and the change can be so small that it is practically inaudible. These results show that causal analysis is useful for understanding the reasoning process of audio classifiers and can be used to successfully manipulate their outputs.

翻译：众所周知，音频分类器常常依赖与音乐无关的特征和虚假相关性进行分类。因此，音频分类器容易被操纵或混淆，导致错误分类。虽然诱发误分类并不困难，但迄今为止，分类器所依赖的特征集尚未得到充分理解。本文提出一种新方法，利用因果推理来发现频率空间中对于给定分类充分且必要的特征。我们在工具FreqReX中实现了该算法，并在多个标准基准数据集上提供了实验结果。实验表明，因果充分且必要的特征子集使我们能够通过极细微地改变输入，以多种方式操纵模型的输出。具体而言，改变240,000个频率中的一个频率，可在58%的情况下导致分类结果改变，且这种改变可以微小到实际听不见的程度。这些结果表明，因果分析有助于理解音频分类器的推理过程，并能成功操纵其输出。