生成AI在信号处理教育中的应用：基于音频基础模型的方法
Generative AI in Signal Processing Education: An Audio Foundation Model Based Approach
AI总结：
本文提出SPEduAFM，通过将生成AI与传统信号处理原理结合，探索音频基础模型在教育中的应用，展示其在自动化讲义转录、互动演示和包容性学习工具中的潜力。
摘要：
音频基础模型（AFMs）作为生成AI的一种专门类别，有潜力通过整合语音和音频增强、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，革新信号处理（SP）教育。本文介绍了SPEduAFM，一种专为SP教育设计的AFM，旨在将传统SP原理与生成AI驱动的创新相结合。通过设想的案例研究，我们概述了AFMs如何支持一系列应用，包括自动讲义转录、互动演示和包容性学习工具，展示其将抽象概念转化为吸引人的实践体验的潜力。本文还探讨了伦理、可解释性和定制化等挑战，通过强调动态、实时的听觉交互来促进体验式和真实的学习。通过将SPEduAFM呈现为一种前瞻性愿景，我们旨在激励更广泛地采用生成AI在工程教育中的应用，提升课堂内外的可访问性、参与度和创新性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
