SART3D: A MATLAB toolbox for spatial audio and signal processing education. Issue 4 (9th May 2019)

• Record Type: Journal Article
• Authors: Cobos, Maximo; Roger, Sandra
• Is Part Of: Computer applications in engineering education. Volume 27:Issue 4(2019)
• Page Start: 971
• Page End: 985
• Publication Date: 2019-05-09
• Subjects: computer assisted learning for electrical engineering -- computer science -- electrical engineering -- engagement -- experiential learning -- signal processing learning -- spatial audio learning; Engineering -- Study and teaching (Higher) -- Periodicals; Engineering -- Computer-assisted instruction -- Periodicals; 620
• DOI: 10.1002/cae.7
• Languages: English
• ISSNs: 1061-3773

Abstract

Abstract: Signal processing courses form usually a common block within the curriculum of electrical engineering and computer science undergraduate studies. While signal processing is the science behind many technologies used in our everyday life, students usually feel that the practical use of signal processing subjects is obscured due to their intensive theoretical perspective. In this context, audio signal processing is a discipline where many general signal processing concepts can be easily experienced with sound. Specifically, spatial audio, i.e., the manipulation of audio signals with the aim of capturing and/or recreating the spatial impression of sound, provides an excellent and appealing application framework for signal processing education. This paper presents SART3D, an open‐source MATLAB toolbox that facilitates spatial audio and signal processing education by enabling students to focus on the implementation and testing of core algorithms and to experience their own results using real‐time processing. The time required for other nonrelevant aspects, such as hardware and driver set‐up or user interaction, is significantly reduced. Experience evaluated throughout four academic years and 45 students (25% female) indicate they felt particularly engaged when SART3D was included in the course, improving their performance and understanding of the subject. In this context, the four factors defined by the Student Course Engagement Questionnaire (skills, emotional, participation, and performance) were analyzed to assess engagement in its multiple dimensions.