Generative AI in Signal Processing Education: An Audio Foundation Model Based Approach

TL;DR

This paper proposes SPEduAFM, a specialized Audio Foundation Model designed to enhance signal processing education through interactive, AI-driven applications and experiential learning tools.

Contribution

Introduction of SPEduAFM, a conceptual Audio Foundation Model tailored for signal processing education to bridge traditional principles with Generative AI innovations.

Findings

Envisioned applications include automated lecture transcription and interactive demonstrations.

Highlights address challenges like ethics, explainability, and customization in AI education.

Aims to inspire adoption of GenAI for accessible, engaging signal processing learning.

Abstract

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.