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Audio Signal Processing for Music Applications

Stanford University and Universitat Pompeu Fabra via Coursera

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In this course you will learn about audio signal processing methodologies that are specific for music and of use in real applications. 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.

Taught by

Xavier Serra and Julius O. Smith III
Cost Free Online Course (Audit)
Pace Upcoming
Subject Digital Media
Provider Coursera
Language English
Hours 8-10 hours a week
Calendar 10 weeks long
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Review for Coursera's Audio Signal Processing for Music Applications
5.0 Based on 1 reviews

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5.0 3 years ago
Alberto completed this course, spending 14 hours a week on it and found the course difficulty to be hard.
This is an applied course mainly based on an audio signal processing package provided to the students.

Main part of the course progresses gradually from sound analysis and synthesis with time-invariant Fourier models (DFT) to time-variant Fourier models (STFT) and finally harmonic+stochastic models, as well as sound transformations derived from each of those models.

There is a combination of python programming assignments, some of them peer-assessed, and sound collections management.

In my opinion, previous DSP knowledge is convenient, as well as programming experience, though not necessarily in python.
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