About this Course
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Intermediate Level

Approx. 52 hours to complete

Suggested: 10 weeks of study, 8 hours/week...

English

Subtitles: English

Skills you will gain

Digital Signal ProcessingSignal ProcessingPython ProgrammingFft Algorithms

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 52 hours to complete

Suggested: 10 weeks of study, 8 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

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....
11 videos (Total 126 min), 1 reading, 2 quizzes
11 videos
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
Basics20m
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....
6 videos (Total 78 min), 1 reading, 2 quizzes
6 videos
DFT 216m
Analyzing a sound8m
Introduction to Freesound12m
Sinusoids14m
DFT15m
1 reading
Advanced readings and videos10m
1 practice exercise
DFT20m
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....
7 videos (Total 99 min), 1 reading, 2 quizzes
7 videos
Fourier properties 213m
Periodic signals11m
Complex sounds9m
Spectrum13m
Fourier properties23m
dftModel13m
1 reading
Advanced readings and videos10m
1 practice exercise
Fourier properties20m
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. ...
6 videos (Total 90 min), 1 reading, 2 quizzes
6 videos
STFT 216m
Spectrogram10m
Analyzing a sound14m
Windows16m
STFT14m
1 reading
Advanced readings and videos10m
1 practice exercise
Short-time Fourier transform20m
Week
5
5 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. ...
8 videos (Total 115 min), 1 reading, 2 quizzes
8 videos
Sinusoidal model 213m
Sinusoidal model 317m
Sinusoidal model13m
Analyzing a sound12m
Peak detection14m
Sinusoidal synthesis12m
sineModel16m
1 reading
Advance reading10m
1 practice exercise
Sinusoidal model20m
Week
6
5 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. ...
7 videos (Total 120 min), 1 reading, 2 quizzes
7 videos
F0 detection20m
Pitch detection14m
Harmonic model25m
Analyzing a sound14m
F0 detection16m
harmonicModel14m
1 reading
Advanced readings10m
1 practice exercise
Harmonic model20m
Week
7
5 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. ...
8 videos (Total 126 min), 1 reading, 2 quizzes
8 videos
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 model20m
Week
8
5 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. ...
9 videos (Total 120 min), 1 reading, 2 quizzes
9 videos
Sounds transformations 216m
Morphing with STFT10m
Time scaling11m
Pitch changes12m
Morphing with HPS12m
stftTransformations18m
sineTransformations11m
hpsTransformations9m
1 reading
Advanced readings10m
1 practice exercise
Sound transformations20m
Week
9
5 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. ...
6 videos (Total 142 min), 2 quizzes
6 videos
Sound and music description24m
Sound descriptors14m
Freesound20m
Intro to Essentia25m
Freesound API26m
1 practice exercise
Sound and music description20m
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. ...
6 videos (Total 106 min), 1 reading, 1 quiz
6 videos
Review12m
MTG-UPF18m
Goodbye17m
Dunya18m
AcousticBrainz22m
1 reading
Advanced readings10m
1 practice exercise
Concluding topics20m
6 hours to complete

Concluding topics: Lesson Choices

...
3 quizzes
4.8
56 ReviewsChevron Right

67%

got a tangible career benefit from this course

Top Reviews

By LNDec 4th 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 HZJan 21st 2017

I learned a lot during this course. It took quite a lot of time and energy to complete it, but I'm glad I did. It is now much easier to follow the text of Richard Lyons' book. Highly recommended.

Instructors

Avatar

Xavier Serra

Full Professor
Dept. of Information and Communication Technologies, UPF
Avatar

Prof Julius O Smith, III

Professor of Music and (by courtesy) Electrical Engineering
CCRMA

About 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....

About 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....

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • Yes, there is no fee in this course. You can follow the course, do the assignments, and obtain a final grade completely for free.

  • No, we do not offer this option.

  • All the materials and tools for the class are available online under open licences.

  • No, it is self-contained.

  • All the assignments start from some existing Python code that the student have to understand and modify. Some programming experience is necessary.

  • You will play around with sounds a lot, analysing them, transforming them, and making interesting new sounds.

More questions? Visit the Learner Help Center.