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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. 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.
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Rating 4.7 based on 63 ratings
Length 11 weeks
Effort 10 weeks of study, 8 hours/week
Starts Dec 19 (next week)
Cost $0
From Universitat Pompeu Fabra of Barcelona, Stanford University via Coursera
Instructors Xavier Serra, Prof Julius O Smith, III
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Art & Design Humanities
Tags Computer Science Arts And Humanities Music And Art Software Development

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What people are saying

audio signal processing

This is an applied course mainly based on an audio signal processing package provided to the students.

This course is amazing I wish I can give 6 stars best course to apply your math knowledment great course, learned a lot for it A very good introduction to audio signal processing .

It covers all the aspects of the topics discussed on Audio Signal Processing ranging from In-depth Theory to excellent examples & demonstration to programming aspects.

This course gives quite some fundumental concepts about the audio signal processing.

A very good resource to start learning audio signal processing!

An absolutely awesome introduction to Audio Signal Processing.

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well explained

Everything was well explained and well structured.

The course is very well explained, the assignment are really interesting and the topic is very interesting as well.

Very well explained and organized course material.

Very well explained, good examples, excellent learning material, practical exercises, and lots and lots of room for further personal study!

Very interesting and well explained Now it can be taken under Windows the course is just great!

every topic is very well explained Excellent course, the best I have been on (and I did 20+ MOOCs ;-) great course to learn basis of signal processing.

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fourier transform

I still do not understand Fourier Transform, why it is symmetric and one half of it is negative, why there are imaginary numbers, and what imaginary numbers mean.

I understood not only mathematics of Fourier Transform, but also the meaning of each step.

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.

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very interesting

Very interesting subject!

Very interesting and useful class For a free course this was amazing.

Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Audio Transcriber $55k

Audio Engineer 4 $57k

Audio Production $58k

Freelance Audio $59k

Signal Officer 3 $61k

AUDIO ENERGY $68k

Audio Sales $69k

Mixed Signal Audio Applications Engineer $70k

Audio and Speech Signal Processing Engineer $73k

Supervisor Audio $81k

Signal Analyst $82k

Signal Engineer 1 $99k

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Rating 4.7 based on 63 ratings
Length 11 weeks
Effort 10 weeks of study, 8 hours/week
Starts Dec 19 (next week)
Cost $0
From Universitat Pompeu Fabra of Barcelona, Stanford University via Coursera
Instructors Xavier Serra, Prof Julius O Smith, III
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Art & Design Humanities
Tags Computer Science Arts And Humanities Music And Art Software Development

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