understand the math behind the Discrete Fourier Transform(DFT), one of the most useful formulas in applied math and computer science.
gain a deeper appreciation for the DFT by applying it to simple applications using Python
be able to mathematically and programmatically determine note/chord of a sound file using the DFT in Python
understand the basics of digital audio
be able to filter out noise from a sound file using Python
be able to understand the basics of sound compression and filtering
understand the difference between time domains vs. frequency domains of digital signals
implement your own version of the Discrete Fourier Transform in Python and compare it to the efficient Fast Fourier Transform
understand why the DFT works
understand the DFT from the geometric viewpoint via the inner product
be able to programmatically play sounds from .txt and .wav files
understand the basics of sounds, music theory, physics of sounds
6 sections • 6 lectures • 2h 8m total length
Digital Audio Basics
Aliasing and the Nyquist Frequency
Understanding the Discrete Fourier Transform
A Simple Filtering Application
Some programming in Python is helpful but not necessary. Most of the programming is done with very little/easy Python code.
Some precalculus is helpful, including basic complex numbers and basic sine/cosine graphs, all of which is reviewed.
The Discrete Fourier Transform (DFT) is one of the most useful algorithms in computer science and digital signal processing. If you've ever opened a JPEG, listened to an MP3, watched a MPEG video, or used voice recognition of Alexa or the Shazam app, you've used some variant of the DFT. Its efficient implementation, the Fast Fourier Transform, is considered one of the top 10 algorithms of the 20th century by the American Institute of Physics and the IEEE Computer Society. Fortunately, the math is very accessible and only involves basic complex numbers and basic trigonometry.
This course is a very basic introduction to the Discrete Fourier Transform. We will focus on understanding the math behind the formula and use Python to do some simple applications of the DFT and fully appreciate its utility. . The Python code we are writing is, however, very minimal. Students with little or no programming experience will still be able to learn Python through the examples in the lectures.
Students should have Anaconda installed with the Jupyter Notebook. However, all of the programming assignments can also be run on the cloud on MyBinder.
Who this course is for:
anyone who wishes to understand the Discrete Fourier Transform and its applications
anyone who wishes to understand a beautiful concept that lies at the intersection of math and music
anyone who wishes to see a powerful and interesting application of Python and Numpy.
I am currently a faculty at Boston Latin School and a lecturer at the University of Massachusetts Boston(Umass Boston). I received both my Masters and Ph.D. in Mathematics at Brigham Young University. I am passionate about teaching and my interest is to bring interesting ideas in math and computer science accessible to a wide audience.