Interpreting the decisions of deep learning models, including audio
classifiers, is crucial for ensuring the transparency and trustworthiness of
this technology. In this paper, we introduce LMAC-ZS (Listenable Maps for Audio
Classifiers in the Zero-Shot context), which, to the best of our knowledge, is
the first decoder-based post-hoc interpretation method for explaining the
decisions of zero-shot audio classifiers. The proposed method utilizes a novel
loss function that maximizes the faithfulness to the original similarity
between a given text-and-audio pair. We provide an extensive evaluation using
the Contrastive Language-Audio Pretraining (CLAP) model to showcase that our
interpreter remains faithful to the decisions in a zero-shot classification
context. Moreover, we qualitatively show that our method produces meaningful
explanations that correlate well with different text prompts.

这篇论文介绍了一种解释零样本音频分类器决策的基于解码器的事后解释方法 LMAC-ZS，并通过使用 CLAP 模型进行评估，显示该方法在零样本分类环境中保持了对决策的忠实，并且与不同的文本提示相关的产生了有意义的解释。