A Framework for Evaluating Faithfulness in Explainable AI for Machine Anomalous Sound Detection Using Frequency-Band Perturbation
Abstract
Explainable AI (XAI) is commonly applied to anomalous sound detection (ASD) models to identify which time-frequency regions of an audio signal contribute to an anomaly decision. However, most audio explanations rely on qualitative inspection of saliency maps, leaving open the question of whether these attributions accurately reflect the spectral cues the model uses. In this work, we introduce a new quantitative framework for evaluating XAI faithfulness in machine-sound analysis by directly linking attribution relevance to model behaviour through systematic frequency-band removal. This approach provides an objective measure of whether an XAI method for machine ASD correctly identifies frequency regions that influence an ASD model's predictions. By using four widely adopted methods, namely Integrated Gradients, Occlusion, Grad-CAM and SmoothGrad, we show that XAI techniques differ in reliability, with Occlusion demonstrating the strongest alignment with true model sensitivity and gradient-+based methods often failing to accurately capture spectral dependencies. The proposed framework offers a reproducible way to benchmark audio explanations and enables more trustworthy interpretation of spectrogram-based ASD systems.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents a comprehensive framework for evaluating the faithfulness of XAI methods in machine anomalous sound detection, significantly advancing the interpretability of audio-based machine learning models. The rigorous methodology and detailed experimental evaluation contribute to a deeper understanding of model behavior, with potential applications in safety-critical industrial settings.
Methodology
The paper introduces a novel quantitative framework for evaluating the faithfulness of explainable AI (XAI) methods in the context of machine anomalous sound detection (ASD). The methodology effectively links attribution relevance to model behavior through systematic frequency-band removal, allowing for a robust assessment of various XAI techniques. By employing four widely adopted methods (Integrated Gradients, Occlusion, Grad-CAM, and SmoothGrad), the authors provide a comparative analysis that highlights the strengths and weaknesses of each method in capturing model sensitivity to frequency bands. This rigorous approach is a significant advancement over the qualitative inspection methods that dominate the field.
Results & evaluation
The experiments are well-structured, utilizing the DCASE2023 Task 2 ASD dataset to evaluate the proposed framework. The paper conducts three main experiments: a qualitative comparison of XAI methods, a frequency-band importance analysis, and a faithfulness evaluation of the XAI methods. The results demonstrate clear differences in the performance of the XAI techniques, with Occlusion showing the strongest correlation with model behavior. The use of statistical measures such as Spearman's rank correlation coefficient enhances the rigor of the evaluation.
Reproducibility
The paper provides sufficient details regarding the experimental setup, including the dataset, model architecture, and spectrogram extraction process. However, the absence of a publicly available code repository limits the reproducibility of the results. Future work should include sharing the implementation to facilitate validation by other researchers.
Limitations
The study primarily focuses on frequency-based perturbations, neglecting temporal aspects that may also influence model sensitivity. Additionally, the use of linear frequency bands rather than perceptually informed scales could limit the applicability of the findings in practical settings. The evaluation framework is also tailored to a specific convolutional architecture, which may not generalize to other model types.
Broader impact
The findings have significant implications for the development of interpretable machine learning systems in safety-critical applications, such as industrial monitoring and maintenance. By providing a reliable method for assessing the faithfulness of XAI techniques, this work can help practitioners better understand model behavior and improve decision-making processes based on model predictions. The proposed framework could also inspire further research into XAI methods tailored for audio and other domains. This paper presents a comprehensive framework for evaluating the faithfulness of XAI methods in machine anomalous sound detection, significantly advancing the interpretability of audio-based machine learning models. The rigorous methodology and detailed experimental evaluation contribute to a deeper understanding of model behavior, with potential applications in safety-critical industrial settings.
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