An approach to STFT and CWT learning through music hands‐on labs

Abstract

AbstractThis work presents a series of lectures and activities for Digital Signal Processing (DSP) teaching, based upon music and their principal elements such as melody, pitch, timbre, beat, and metric, to explain the time‐frequency analysis and its repercussions in other areas of engineering. DSP courses are difficult to the students due to a high mathematical content and high level of abstraction, for this reason, the primary objective of this work is to create a pedagogical tool that allows the improvement on the teaching process in Signal Processing courses. There are two popular approaches: Short‐Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) which have been used to find time‐frequency maps. The student roll was designed for active participation by creating melodies from real instruments as well as synthetic generators, and by implementing the codes of STFT and CWT in MATLAB. The results demonstrate that in general terms, after carrying out these laboratory activities the students are more motivated to learn Signal Processing theory, and some of them become interested in the new research line on Music Information Retrieval (MIR), which would benefit the continuity of the Accreditation Board for Engineering and Technologies ABET, obtained for the Automation Engineering program at Universidad Autónoma de Querétaro (UAQ) since 2016.