我猜这就是为什么它被称为蓝调：用于音频分类器的因果分析
I Guess That's Why They Call it the Blues: Causal Analysis for Audio Classifiers
AI总结：
本文提出利用因果推理发现音频分类器中对分类至关重要的频率特征，并通过实验展示其对模型输出的操控能力。
摘要：
众所周知，音频分类器常常依赖于非音乐相关特征和虚假相关性来对音频进行分类。因此音频分类器容易被操控或混淆，导致错误分类。虽然诱导错误分类并不困难，但直到现在，分类器所依赖的特征集仍未被充分理解。在本文中，我们介绍了一种新的方法，该方法利用因果推理来发现频率空间中对给定分类足够且必要的特征。我们描述了该算法在工具FreqReX中的实现，并在多个标准基准数据集上提供了实验结果。我们的实验表明，因果充分且必要的子集允许通过略微改变输入来以多种方式操控模型的输出。具体而言，对24万个频率中的一个进行更改，有58%的时间会导致分类结果的改变，而且这种变化可以非常微小，以至于几乎听不到。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，并可以成功地操控其输出。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
