Gratuit

Offert par

Université Pompeu Fabra de Barcelone

Université de Stanford

Traitement des signaux audios pour des applications musicales

Université Pompeu Fabra de Barcelone

À propos de ce cours

55,120 consultations récentes

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.

Résultats de carrière des étudiants

67%

ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau intermédiaire

Approx. 54 heures pour terminer

Anglais

Sous-titres : Anglais

Compétences que vous acquerrez

Digital Signal ProcessingSignal ProcessingPython ProgrammingFft Algorithms

Résultats de carrière des étudiants

67%

ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau intermédiaire

Approx. 54 heures pour terminer

Anglais

Sous-titres : Anglais

Enseignants

Évaluation de l'enseignant

5/5 (5 notes)

Xavier Serra

Full Professor

Dept. of Information and Communication Technologies, UPF

36,955 étudiants

1 cours

Prof Julius O Smith, III

Professor of Music and (by courtesy) Electrical Engineering

CCRMA

36,955 étudiants

1 cours

Offert par

Université Pompeu Fabra de Barcelone

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.

Université de Stanford

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.

Programme du cours : ce que vous apprendrez dans ce cours

Évaluation du contenu96%(5,540 notes)

Semaine

Semaine 1

6 heures pour terminer

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 heures pour terminer

11 vidéos (Total 126 min), 1 lecture, 2 quiz

11 vidéos

Teaser3 min

Welcome4 min

Introduction to Audio Signal Processing13 min

Course outline10 min

Basic mathematics16 min

Introduction to Audacity9 min

Introduction to SonicVisualizer10 min

Introduction to sms-tools17 min

Introduction to Python11 min

Python and sounds13 min

sms-tools software14 min

1 lecture

Advanced readings and videos10 min

1 exercice pour s'entraîner

Basics20 min

Semaine

Semaine 2

5 heures pour terminer

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 heures pour terminer

6 vidéos (Total 78 min), 1 lecture, 2 quiz

6 vidéos

DFT 111 min

DFT 216 min

Analyzing a sound8 min

Introduction to Freesound12 min

Sinusoids14 min

DFT15 min

1 lecture

Advanced readings and videos10 min

1 exercice pour s'entraîner

DFT20 min

Semaine

Semaine 3

5 heures pour terminer

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 heures pour terminer

7 vidéos (Total 99 min), 1 lecture, 2 quiz

7 vidéos

Fourier properties 114 min

Fourier properties 213 min

Periodic signals11 min

Complex sounds9 min

Spectrum13 min

Fourier properties23 min

dftModel13 min

1 lecture

Advanced readings and videos10 min

1 exercice pour s'entraîner

Fourier properties20 min

Semaine

Semaine 4

5 heures pour terminer

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 heures pour terminer

6 vidéos (Total 90 min), 1 lecture, 2 quiz

6 vidéos

STFT 117 min

STFT 216 min

Spectrogram10 min

Analyzing a sound14 min

Windows16 min

STFT14 min

1 lecture

Advanced readings and videos10 min

1 exercice pour s'entraîner

Short-time Fourier transform20 min

Semaine

Semaine 5

5 heures pour terminer

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.

5 heures pour terminer

8 vidéos (Total 115 min), 1 lecture, 2 quiz

8 vidéos

Sinusoidal model 115 min

Sinusoidal model 213 min

Sinusoidal model 317 min

Sinusoidal model13 min

Analyzing a sound12 min

Peak detection14 min

Sinusoidal synthesis12 min

sineModel16 min

1 lecture

Advance reading10 min

1 exercice pour s'entraîner

Sinusoidal model20 min

Semaine

Semaine 6

5 heures pour terminer

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.

5 heures pour terminer

7 vidéos (Total 120 min), 1 lecture, 2 quiz

7 vidéos

Harmonic model13 min

F0 detection20 min

Pitch detection14 min

Harmonic model25 min

Analyzing a sound14 min

F0 detection16 min

harmonicModel14 min

1 lecture

Advanced readings10 min

1 exercice pour s'entraîner

Harmonic model20 min

Semaine

Semaine 7

5 heures pour terminer

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.

5 heures pour terminer

8 vidéos (Total 126 min), 1 lecture, 2 quiz

8 vidéos

Stochastic model20 min

Sinusoidal plus residual modeling16 min

Stochastic model10 min

Harmonic plus residual model14 min

Harmonic plus stochastic model12 min

stochasticModel17 min

hprModel19 min

hpsModel14 min

1 lecture

Advanced readings10 min

1 exercice pour s'entraîner

Sinusoidal plus residual model20 min

Semaine

Semaine 8

5 heures pour terminer

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.

5 heures pour terminer

9 vidéos (Total 120 min), 1 lecture, 2 quiz

9 vidéos

Sounds transformations 116 min

Sounds transformations 216 min

Morphing with STFT10 min

Time scaling11 min

Pitch changes12 min

Morphing with HPS12 min

stftTransformations18 min

sineTransformations11 min

hpsTransformations9 min

1 lecture

Advanced readings10 min

1 exercice pour s'entraîner

Sound transformations20 min

Semaine

Semaine 9

5 heures pour terminer

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.

5 heures pour terminer

6 vidéos (Total 142 min)

6 vidéos

Audio features30 min

Sound and music description24 min

Sound descriptors14 min

Freesound20 min

Intro to Essentia25 min

Freesound API26 min

1 exercice pour s'entraîner

Sound and music description20 min

Semaine

Semaine 10

2 heures pour terminer

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 heures pour terminer

6 vidéos (Total 106 min), 1 lecture, 1 quiz

6 vidéos

Beyond audio processing16 min

Review12 min

MTG-UPF18 min

Goodbye17 min

Dunya18 min

AcousticBrainz22 min

1 lecture

Advanced readings10 min

1 exercice pour s'entraîner

Concluding topics20 min

6 heures pour terminer

Concluding topics: Lesson Choices

6 heures pour terminer

Foire Aux Questions

Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
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.

D'autres questions ? Visitez le Centre d'Aide pour les Etudiants.