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

Muhammad Salman Khan, Ahmad Ullah, Siddique Latif, Junaid Qadir

TL;DR

The paper argues that Audio Foundation Models (AFMs), a class of Generative AI, can fundamentally transform signal processing education by enabling real-time, interactive, and multimodal learning experiences. It introduces SPEduAFM, a DSP-focused AFM concept, and outlines a phased development path—from fine-tuning existing AFMs to building a bespoke SPEduAFM platform and demonstrations for experiential learning. Key contributions include a structured vision for integrating AFMs into SP labs, a comparison of traditional labs with AI-enhanced SPEduAFM methods, and practical considerations for ethics, explainability, and accessibility. The work highlights potential educational benefits such as automated transcription, personalized tutoring, and multimodal demonstrations, while acknowledging remaining challenges and future research directions in human-centric GenAI education. Overall, SPEduAFM is presented as a forward-looking framework to bridge theory and practice in SP through engaging, accessible, and authentic learning experiences.

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.

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

TL;DR

The paper argues that Audio Foundation Models (AFMs), a class of Generative AI, can fundamentally transform signal processing education by enabling real-time, interactive, and multimodal learning experiences. It introduces SPEduAFM, a DSP-focused AFM concept, and outlines a phased development path—from fine-tuning existing AFMs to building a bespoke SPEduAFM platform and demonstrations for experiential learning. Key contributions include a structured vision for integrating AFMs into SP labs, a comparison of traditional labs with AI-enhanced SPEduAFM methods, and practical considerations for ethics, explainability, and accessibility. The work highlights potential educational benefits such as automated transcription, personalized tutoring, and multimodal demonstrations, while acknowledging remaining challenges and future research directions in human-centric GenAI education. Overall, SPEduAFM is presented as a forward-looking framework to bridge theory and practice in SP through engaging, accessible, and authentic learning experiences.

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.
Paper Structure (35 sections, 2 figures, 2 tables)

This paper contains 35 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. State-of-the-Art in Audio Foundation Models
  3. Key Characteristics of AFMs
  4. Multimodal Capabilities and Signal Processing
  5. Recent Innovations in Audio Foundation Models
  6. New AFM Developments and Tools
  7. What's New and What Remains Constant
  8. Educational Applications of AFMs
  9. Personalized Education
  10. Inclusive Education and Accessibility
  11. Intelligent Tutoring Systems (ITS)
  12. Automated Feedback and Assessment
  13. Engagement Through Gamification and Interaction
  14. Versatile Learning Inside and Outside the Classroom
  15. AFMs for Supporting Teachers
  16. ...and 20 more sections

Figures (2)

  • Figure 1: Taxonomy of Signal Processing Techniques for Audio Foundation Models (AFMs).
  • Figure 2: Taxonomy of AFM Applications in Education