Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1342-1345. doi: 10.1109/EMBC48229.2022.9871291.
Since the emergence of the COVID-19 pandemic, various methods to detect the illness from cough and speech audio data have been proposed. While many of them deliver promising results, they lack transparency in the form of expla-nations which is crucial for establishing trust in the classifiers. We propose CoughLIME which extends LIME to explanations for audio data, specifically tailored towards cough data. We show that CoughLIME is capable of generating faithful sonified explanations for COVID-19 detection. To quantify the performance of the explanations generated for the CIdeR model, we adopt pixel flipping to audio and introduce a novel metric to assess the performance of the XAI classifier. CoughLIME achieves a ΔAUC of 19.48 % generating explanations for CIdeR's predictions.
自 COVID-19 大流行以来，已经提出了各种从咳嗽和语音音频数据中检测疾病的方法。虽然其中许多方法都取得了有希望的结果，但它们在解释方面缺乏透明度，而解释对于建立对分类器的信任至关重要。我们提出了 CoughLIME，它将 LIME 扩展到了针对音频数据的解释，特别是针对咳嗽数据进行了定制。我们表明，CoughLIME 能够为 COVID-19 检测生成忠实的声谱解释。为了量化为 CIdeR 模型生成的解释的性能，我们采用像素翻转到音频，并引入了一种新的度量标准来评估 XAI 分类器的性能。CoughLIME 为 CIdeR 的预测生成解释时，AUC 提高了 19.48%。
