用生成式AI打造音频教育模型，让信号处理学习更直观有趣。
Generative AI in Signal Processing Education: An Audio Foundation Model Based Approach
- 构建面向信号处理教育的音频基础模型SPEduAFM，融合传统原理与生成式AI。
- 实现自动课件转录、互动演示等应用，将抽象概念变实践体验。
- 强调实时音频交互，适合工程教育创新者与关注可解释性的教师。
音频基础模型（AFMs）作为生成式AI（GenAI）的一个专门类别，有望通过集成语音与音频增强、去噪、源分离、特征提取、自动分类和实时信号分析等核心应用，彻底改变信号处理（SP）教育。本文提出SPEduAFM——一个面向SP教育的概念性AFM，连接传统SP原理与生成式AI驱动的创新。通过设想的案例研究，阐述了AFMs如何支持自动讲座转录、互动演示和包容性学习工具，展示其将抽象概念转化为生动实践的潜力。论文还探讨了伦理、可解释性和定制化等挑战，强调动态实时音频交互对促进体验式与真实学习的重要性。以前瞻视角呈现SPEduAFM，旨在推动生成式AI在工程教育中的广泛应用，提升课堂内外的可及性、参与度与创新力。
原文摘要 · Abstract (English)
Audio Foundation Models (AFMs), a specialized category of Generative AI (GenAI), have the potential to transform signal processing (SP) education by integrating core applications such as speech and audio enhancement, denoising, source separation, feature extraction, automatic classification, and real-time signal analysis into learning and research. This paper introduces SPEduAFM, a conceptual AFM tailored for SP education, bridging traditional SP principles with GenAI-driven innovations. Through an envisioned case study, we outline how AFMs can enable a range of applications, including automated lecture transcription, interactive demonstrations, and inclusive learning tools, showcasing their potential to transform abstract concepts into engaging, practical experiences. This paper also addresses challenges such as ethics, explainability, and customization by highlighting dynamic, real-time auditory interactions that foster experiential and authentic learning. By presenting SPEduAFM as a forward-looking vision, we aim to inspire broader adoption of GenAI in engineering education, enhancing accessibility, engagement, and innovation in the classroom and beyond.
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