4.8
stars
281 ratings

Xavier Serra +1 more instructor

52,086 already enrolled

Offered By

Universitat Pompeu Fabra of Barcelona

Stanford University

Audio Signal Processing for Music Applications

Universitat Pompeu Fabra of Barcelona

About this Course

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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.

Flexible deadlines

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Coursera Labs

Includes hands on learning projects.

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Intermediate Level

Approx. 49 hours to complete

English

Skills you will gain

Digital Signal Processing
Signal Processing
Python Programming
Fft Algorithms

Flexible deadlines

Reset deadlines in accordance to your schedule.

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Intermediate Level

Approx. 49 hours to complete

English

Instructors

Instructor rating

4.87/5 (32 Ratings)

Xavier Serra

Full Professor

Dept. of Information and Communication Technologies, UPF

52,086 Learners

1 Course

Prof Julius O Smith, III

Professor of Music and (by courtesy) Electrical Engineering

CCRMA

52,086 Learners

1 Course

Offered by

Universitat Pompeu Fabra of Barcelona

Pompeu Fabra University (UPF) is a modern public university, conveniently located in the centre of Barcelona (Spain) with the aim of providing top quality education and standing out as a research-based university. UPF is both a specialised university with a unique teaching model and a cutting-edge research institution. UPF places a strong emphasis on quality teaching, based on comprehensive education and student-centred learning, and innovation in the learning processes. UPF’s MOOCs are produced within this general goal.

Stanford University

Syllabus - What you will learn from this course

Content Rating96%(6,940 ratings)

Week1

Week 1

6 hours to complete

Introduction

6 hours to complete

11 videos (Total 126 min), 1 reading, 2 quizzes

Week2

Week 2

5 hours to complete

Discrete Fourier transform

5 hours to complete

6 videos (Total 78 min), 1 reading, 2 quizzes

Week3

Week 3

5 hours to complete

Fourier theorems

Linearity, shift, symmetry, convolution; energy conservation and decibels; phase unwrapping; zero padding; Fast Fourier Transform and zero-phase windowing; and analysis/synthesis. Demonstration of the analysis of simple periodic signals and of complex sounds; demonstration of spectrum analysis tools. Implementing the computation of the spectrum of a sound fragment using Python and presentation of the dftModel functions implemented in the sms-tools package.

5 hours to complete

7 videos (Total 99 min), 1 reading, 2 quizzes

Week4

Week 4

5 hours to complete

Short-time Fourier transform

5 hours to complete

6 videos (Total 90 min), 1 reading, 2 quizzes

Week5

Week 5

6 hours to complete

Sinusoidal model

Sinusoidal model equation; sinewaves in a spectrum; sinewaves as spectral peaks; time-varying sinewaves in spectrogram; sinusoidal synthesis. Demonstration of the sinusoidal model interface of the sms-tools package and its use in the analysis and synthesis of sounds. Implementation of the detection of spectral peaks and of the sinusoidal synthesis using Python and presentation of the sineModel functions from the sms-tools package, explaining how to use them.

6 hours to complete

8 videos (Total 115 min), 1 reading, 2 quizzes

Week6

Week 6

6 hours to complete

Harmonic model

Harmonic model equation; sinusoids-partials-harmonics; polyphonic-monophonic signals; harmonic detection; f0-detection in time and frequency domains. Demonstrations of pitch detection algorithm, of the harmonic model interface of the sms-tools package and of its use in the analysis and synthesis of sounds. Implementation of the detection of the fundamental frequency in the frequency domain using the TWM algorithm in Python and presentation of the harmonicModel functions from the sms-tools package, explaining how to use them.

6 hours to complete

7 videos (Total 120 min), 1 reading, 2 quizzes

Week7

Week 7

4 hours to complete

Sinusoidal plus residual model

Stochastic signals; stochastic model; stochastic approximation of sounds; sinusoidal/harmonic plus residual model; residual subtraction; sinusoidal/harmonic plus stochastic model; stochastic model of residual. Demonstrations of the stochastic model, harmonic plus residual, and harmonic plus stochastic interfaces of the sms-tools package and of its use in the analysis and synthesis of sounds. Presentation of the stochasticModel, hprModel and hpsModel functions implemented in the sms-tools package, explaining how to use them.

4 hours to complete

8 videos (Total 126 min), 1 reading, 2 quizzes

Week8

Week 8

4 hours to complete

Sound transformations

Filtering and morphing using the short-time Fourier transform; frequency and time scaling using the sinusoidal model; frequency transformations using the harmonic plus residual model; time scaling and morphing using the harmonic plus stochastic model. Demonstrations of the various transformation interfaces of the sms-tools package and of Audacity. Presentation of the stftTransformations, sineTransformations and hpsTransformations functions implemented in the sms-tools package, explaining how to use them.

4 hours to complete

9 videos (Total 120 min), 1 reading, 2 quizzes

Week9

Week 9

4 hours to complete

Sound and music description

Extraction of audio features using spectral analysis methods; describing sounds, sound collections, music recordings and music collections. Clustering and classification of sounds. Demonstration of various plugins from SonicVisualiser to describe sound and music signals and demonstration of some advance features of freesound.org. Presentation of Essentia, a C++ library for sound and music description, explaining how to use it from Python. Programming with the Freesound API in Python to download sound collections and to study them.

4 hours to complete

6 videos (Total 142 min)

Week10

Week 10

2 hours to complete

Concluding topics

2 hours to complete

6 videos (Total 106 min), 1 reading, 1 quiz

3 hours to complete

Concluding topics: Lesson Choices

3 hours to complete

Reviews

4.8

88 reviews

5 stars
88.25%
4 stars
8.54%
3 stars
1.77%
2 stars
0.71%
1 star
0.71%

TOP REVIEWS FROM AUDIO SIGNAL PROCESSING FOR MUSIC APPLICATIONS

by SZJan 5, 2023

Interesting lectures and practical exercises provide a strong foundation for entering the field of digital audio processing. Thank you very much!

by LNDec 3, 2016

Top class! Very well explained, good examples, excellent learning material, practical exercises, and lots and lots of room for further personal study! Well done guys, and especially Xavier! Cheers!

by NAAug 17, 2020

Amazing, Challenging and honestly learnt a lot. This course helped me decide my masters specialisation and I am genuinely excited to pursue it!!

by DAJun 6, 2017

Excellent material for an interesting subject. The video lectures are very good. The course does need some updates for Python3 and associated libraries to stay current.

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Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
Can I take this course for free?
Yes, there is no fee in this course. You can follow the course, do the assignments, and obtain a final grade completely for free.
Can I pay to get a Course Certificate?
No, we do not offer this option.
What resources will I need for this class?
All the materials and tools for the class are available online under open licences.
Do I need to buy a textbook for the course?
No, it is self-contained.
How much programming background is needed for the course?
All the assignments start from some existing Python code that the student have to understand and modify. Some programming experience is necessary.
What is the coolest thing I'll learn if I take this class?
You will play around with sounds a lot, analysing them, transforming them, and making interesting new sounds.

More questions? Visit the Learner Help Center.

Audio Signal Processing for Music Applications

Audio Signal Processing for Music Applications

About this Course

Skills you will gain