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.
Traitement des signaux audios pour des applications musicales
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
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Learner Career Outcomes
67%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
Compétences que vous acquerrez
Digital Signal ProcessingSignal ProcessingPython ProgrammingFft Algorithms
Learner Career Outcomes
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. 53 heures pour terminer
Recommandé : 10 weeks of study, 8 hours/week
Anglais
Sous-titres : Anglais
Enseignants
Xavier Serra
Full ProfessorDept. of Information and Communication Technologies, UPF
33,954 étudiants
1 cours
Prof Julius O Smith, III
Professor of Music and (by courtesy) Electrical EngineeringCCRMA
33,954 étudiants1 coursOffert 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
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
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
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
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
1 lecture
Advanced readings and videos10 min
1 exercice pour s'entraîner
Short-time Fourier transform20 min
4.8
62 avisMeilleurs avis pour Traitement des signaux audios pour des applications musicales
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!
An absolutely awesome introduction to Audio Signal Processing. The additive introduction of new concepts is capable of teaching any beginner this topic which ordinarily is difficult to understand.
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.