让文本生成音频的模型变得更透明，看清每个词对声音的影响。
AudioGenX: Explainability on Text-to-Audio Generative Models
- 通过事实与反事实目标函数优化解释器，定位文本词对音频的贡献。
- 在音频标记级别提供忠实解释，实验验证效果优于现有方法。
- 适合关注AI音频生成可解释性的研究者与开发者使用。
文本到音频生成模型（TAG）在根据文本描述生成音频方面取得了显著进展。然而，一个关键挑战在于缺乏对每个文本输入如何影响生成音频的透明度。为解决这一问题，我们提出AudioGenX，一种可解释人工智能（XAI）方法，通过突出输入词元的重要性，为文本到音频生成模型提供解释。AudioGenX通过利用事实与反事实目标函数优化解释器，在音频标记级别提供忠实的解释。该方法深入揭示了文本输入与音频输出之间的关系，提升了TAG模型的可解释性与可信度。大量实验证明，AudioGenX在生成忠实解释方面表现优异，其性能通过专为音频生成任务设计的新评估指标进行基准测试，优于现有方法。
原文摘要 · Abstract (English)
Text-to-audio generation models (TAG) have achieved significant advances in generating audio conditioned on text descriptions. However, a critical challenge lies in the lack of transparency regarding how each textual input impacts the generated audio. To address this issue, we introduce AudioGenX, an Explainable AI (XAI) method that provides explanations for text-to-audio generation models by highlighting the importance of input tokens. AudioGenX optimizes an Explainer by leveraging factual and counterfactual objective functions to provide faithful explanations at the audio token level. This method offers a detailed and comprehensive understanding of the relationship between text inputs and audio outputs, enhancing both the explainability and trustworthiness of TAG models. Extensive experiments demonstrate the effectiveness of AudioGenX in producing faithful explanations, benchmarked against existing methods using novel evaluation metrics specifically designed for audio generation tasks.
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