Audio Signal Processing for Music Applications

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8/10 stars
based on  2 reviews
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Cost FREE
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Audio Signal Processing for Music Applications

Course Details

Cost

FREE

Upcoming Schedule

  • On demand

Course Provider

Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4680 reviews

Course Description

In this course you will learn about audio signal processing methodologies that are specific for music and of use in real applications. You will learn to analyse, synthesize and transform sounds using the Python programming language.
Reviews 8/10 stars
2 Reviews for Audio Signal Processing for Music Applications

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Steven Frank profile image
Steven Frank profile image
8/10 starsCompleted
  • 59 reviews
  • 57 completed
3 years, 3 months ago
This course covers the ways in which music can be analyzed, represented, changed, and categorized in digital form. Topics covered include Fourier analysis, sinusoidal decomposition of music, harmonic and stochastic representations, sound transformations, and extraction of audio features from music for purposes of classification and retrieval. The course emphasizes not only programming various signal-processing functions in Python, but also the use of very versatile and just plain cool applications such as Audacity, Sonic Visualizer, a variety of spectral modeling synthesis tools, and Essentia. The programming and most applications require a Linux environment, but the course staff has heroically assembled a downloadable image that can be launched in Virtual Box (which is free), and which has all applications pre-loaded and ready to go. Each week, Prof. Serra presents lectures in three formats: a theoretical overview of the mathem... This course covers the ways in which music can be analyzed, represented, changed, and categorized in digital form. Topics covered include Fourier analysis, sinusoidal decomposition of music, harmonic and stochastic representations, sound transformations, and extraction of audio features from music for purposes of classification and retrieval. The course emphasizes not only programming various signal-processing functions in Python, but also the use of very versatile and just plain cool applications such as Audacity, Sonic Visualizer, a variety of spectral modeling synthesis tools, and Essentia. The programming and most applications require a Linux environment, but the course staff has heroically assembled a downloadable image that can be launched in Virtual Box (which is free), and which has all applications pre-loaded and ready to go. Each week, Prof. Serra presents lectures in three formats: a theoretical overview of the mathematical and physical principles, “demo” lectures that illustrate operation of the various tools and applications, and programming lectures that introduce the code underlying resources you’ll use that week as well as useful background for the programming tasks. The theory lectures are excellent, compressing a lot of technical detail into accessible, understandable nuggets. The demo and programming lectures, which are more free-form, are useful but sometimes ramble a bit. In terms of grading, the first six weeks feature computer-graded programming assignments; weeks 7 through 10 have peer-reviewed assignments that involve analysis and manipulation of music clips. There is also a weekly quiz. And now, some cautionary notes. First, there’s quite a bit of (interesting!) math and, particularly as the course progresses, some real Python programming challenges. Familiarity with the fine points of Python array operations is essential. Second, there were problems related to grading. For code submissions, the auto-graders were very fussy: you thought your function returned true or false, but the grader expected a default boolean and unbeknownst to you, your function returned a NumPy boolean and was marked wrong; or you returned an array in the right format, but unbeknowst to you, the grader expected 64-bit ints and yours were 32 bits. The weekly quizzes varied in difficulty but often had at least one question whose “correct” answer was ambiguous or seemed to contradict the lectures. And while some of the topics such as the discrete and short-time Fourier transforms have obvious and widespread importance, you may be forgiven for asking yourself, when all is said and done, how relevant a lot of what you’ve learned is to the experience, understanding, or organization of music outside some very specialized contexts. Still, learning to unpack a flute solo into its harmonic and breathy components, or to gain insight from spectrograms that at first look like a Jackson Pollack viewed through a screen door, is quite rewarding -- and playing with sounds is just fun. If you’re ready to make the commitment and can tolerate a few frustrations, you’ll get a lot out of this course.
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10/10 starsTaking Now
  • 3 reviews
  • 1 completed
3 years, 4 months ago
Great course, great content, great explanations, meaningful and challenging exercises. Staff is very responsive to improvement suggestions and timely bug fixes.
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