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.

Learner Career Outcomes

67%

got a tangible career benefit from this course

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 49 hours to complete

English

Subtitles: French, Portuguese (Brazilian), Russian, English, Spanish

Skills you will gain

Digital Signal ProcessingSignal ProcessingPython ProgrammingFft Algorithms

Learner Career Outcomes

67%

got a tangible career benefit from this course

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 49 hours to complete

English

Subtitles: French, Portuguese (Brazilian), Russian, English, Spanish

Instructors

Instructor rating

5/5 (12 Ratings)

Xavier Serra

Full Professor

Dept. of Information and Communication Technologies, UPF

40,675 Learners

1 Course

Prof Julius O Smith, III

Professor of Music and (by courtesy) Electrical Engineering

CCRMA

40,675 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

The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.

Syllabus - What you will learn from this course

Content Rating96%(6,076 ratings)

Week

Week 1

6 hours to complete

Introduction

Introduction to the course, to the field of Audio Signal Processing, and to the basic mathematics needed to start the course. Introductory demonstrations to some of the software applications and tools to be used. Introduction to Python and to the sms-tools package, the main programming tool for the course.

6 hours to complete

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

11 videos

Teaser3m

Welcome4m

Introduction to Audio Signal Processing13m

Course outline10m

Basic mathematics16m

Introduction to Audacity9m

Introduction to SonicVisualizer10m

Introduction to sms-tools17m

Introduction to Python11m

Python and sounds13m

sms-tools software14m

1 reading

Advanced readings and videos10m

1 practice exercise

Basics30m

Week

Week 2

5 hours to complete

Discrete Fourier transform

The Discrete Fourier Transform equation; complex exponentials; scalar product in the DFT; DFT of complex sinusoids; DFT of real sinusoids; and inverse-DFT. Demonstrations on how to analyze a sound using the DFT; introduction to Freesound.org. Generating sinusoids and implementing the DFT in Python.

5 hours to complete

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

6 videos

DFT 111m

DFT 216m

Analyzing a sound8m

Introduction to Freesound12m

Sinusoids14m

DFT15m

1 reading

Advanced readings and videos10m

1 practice exercise

DFT30m

Week

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

7 videos

Fourier properties 114m

Fourier properties 213m

Periodic signals11m

Complex sounds9m

Spectrum13m

Fourier properties23m

dftModel13m

1 reading

Advanced readings and videos10m

1 practice exercise

Fourier properties30m

Week

Week 4

5 hours to complete

Short-time Fourier transform

STFT equation; analysis window; FFT size and hop size; time-frequency compromise; inverse STFT. Demonstration of tools to compute the spectrogram of a sound and on how to analyze a sound using them. Implementation of the windowing of sounds using Python and presentation of the STFT functions from the sms-tools package, explaining how to use them.

5 hours to complete

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

6 videos

STFT 117m

STFT 216m

Spectrogram10m

Analyzing a sound14m

Windows16m

STFT14m

1 reading

Advanced readings and videos10m

1 practice exercise

Short-time Fourier transform30m

Week

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

8 videos

Sinusoidal model 115m

Sinusoidal model 213m

Sinusoidal model 317m

Sinusoidal model13m

Analyzing a sound12m

Peak detection14m

Sinusoidal synthesis12m

sineModel16m

1 reading

Advance reading10m

1 practice exercise

Sinusoidal model30m

Week

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

7 videos

Harmonic model13m

F0 detection20m

Pitch detection14m

Harmonic model25m

Analyzing a sound14m

F0 detection16m

harmonicModel14m

1 reading

Advanced readings10m

1 practice exercise

Harmonic model30m

Week

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

8 videos

Stochastic model20m

Sinusoidal plus residual modeling16m

Stochastic model10m

Harmonic plus residual model14m

Harmonic plus stochastic model12m

stochasticModel17m

hprModel19m

hpsModel14m

1 reading

Advanced readings10m

1 practice exercise

Sinusoidal plus residual model30m

Week

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

9 videos

Sounds transformations 116m

Sounds transformations 216m

Morphing with STFT10m

Time scaling11m

Pitch changes12m

Morphing with HPS12m

stftTransformations18m

sineTransformations11m

hpsTransformations9m

1 reading

Advanced readings10m

1 practice exercise

Sound transformations30m

Week

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)

6 videos

Audio features30m

Sound and music description24m

Sound descriptors14m

Freesound20m

Intro to Essentia25m

Freesound API26m

1 practice exercise

Sound and music description30m

Week

Week 10

2 hours to complete

Concluding topics

Audio signal processing beyond this course. Beyond audio signal processing. Review of the course topics. Where to learn more about the topics of this course. Presentation of MTG-UPF. Demonstration of Dunya, a web browser to explore several audio music collections, and of AcousticBrainz, a collaborative initiative to collect and share music data.

2 hours to complete

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

6 videos

Beyond audio processing16m

Review12m

MTG-UPF18m

Goodbye17m

Dunya18m

AcousticBrainz22m

1 reading

Advanced readings10m

1 practice exercise

Concluding topics30m

3 hours to complete

Concluding topics: Lesson Choices

3 hours to complete

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

Learner Career Outcomes

67%

Skills you will gain

Learner Career Outcomes

67%

Instructors

Xavier Serra

Prof Julius O Smith, III

Offered by

Universitat Pompeu Fabra of Barcelona

Stanford University

Syllabus - What you will learn from this course

Week 1

Introduction

Week 2

Discrete Fourier transform

Week 3

Fourier theorems

Week 4

Short-time Fourier transform

Week 5

Sinusoidal model

Week 6

Harmonic model

Week 7

Sinusoidal plus residual model

Week 8

Sound transformations

Week 9

Sound and music description

Week 10

Concluding topics

Concluding topics: Lesson Choices

Reviews

TOP REVIEWS FROM AUDIO SIGNAL PROCESSING FOR MUSIC APPLICATIONS

Frequently Asked Questions

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