Coursera Stanford University And UPF Audio Signal Processing for Music Applications TUTORiAL

Audio Signal Processing for Music Applications
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.

Instructors

Prof Xavier Serra
Universitat Pompeu Fabra of Barcelona
Prof Julius O Smith, III
Stanford University

About the Course
Audio signal processing is an engineering field that focuses on the computational methods for intentionally altering sounds, methods that are used in many musical applications.

We have tried to put together a course that can be of interest and accessible to people coming from diverse backgrounds while going deep into several signal processing topics. We focus on the spectral processing techniques of relevance for the description and transformation of sounds, developing the basic theoretical and practical knowledge with which to analyze, synthesize, transform and describe audio signals in the context of music applications.

The course is based on open software and content. The demonstrations and programming exercises are done using Python under Ubuntu, and the references and materials for the course come from open online repositories. We are also distributing with open licenses the software and materials developed for the course.

Course Syllabus

Week 1: Introduction; basic mathematics

Week 2: Discrete Fourier transform

Week 3: Fourier transform properties

Week 4: Short-time Fourier transform

Week 5: Sinusoidal model

Week 6: Harmonic model

Week 7: Sinusoidal plus residual modeling

Week 8: Sound transformations

Week 9: Sound/music description

Week 10: Concluding topics; beyond audio signal processing

Recommended Background
The course assumes some basic background in mathematics and signal processing. Also, since the assignments are done with the programming language Python, some software programming background in any language is most helpful.

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