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.
Processamento de sinais de áudio para aplicativos musicais
oferecido por
Universidade Pompeu Fabra de Barcelona
Universidade de Stanford
Universidade Pompeu Fabra de Barcelona
Informações sobre o curso
4.8
204 classificações
Habilidades que você terá
Digital Signal ProcessingSignal ProcessingPython ProgrammingFft Algorithms
Programa - O que você aprenderá com este curso
Semana
16 horas para concluir
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 vídeos (total de (Total 126 mín.) min), 1 leitura, 2 testes
11 videos
Teaser3min
Welcome4min
Introduction to Audio Signal Processing13min
Course outline10min
Basic mathematics16min
Introduction to Audacity9min
Introduction to SonicVisualizer10min
Introduction to sms-tools17min
Introduction to Python11min
Python and sounds13min
sms-tools software14min
1 leituras
Advanced readings and videos10min
1 exercício prático
Basics20min
Semana
25 horas para concluir
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 vídeos (total de (Total 78 mín.) min), 1 leitura, 2 testes
6 videos
DFT 111min
DFT 216min
Analyzing a sound8min
Introduction to Freesound12min
Sinusoids14min
DFT15min
1 leituras
Advanced readings and videos10min
1 exercício prático
DFT20min
Semana
35 horas para concluir
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 vídeos (total de (Total 99 mín.) min), 1 leitura, 2 testes
7 videos
Fourier properties 114min
Fourier properties 213min
Periodic signals11min
Complex sounds9min
Spectrum13min
Fourier properties23min
dftModel13min
1 leituras
Advanced readings and videos10min
1 exercício prático
Fourier properties20min
Semana
45 horas para concluir
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 vídeos (total de (Total 90 mín.) min), 1 leitura, 2 testes
1 leituras
Advanced readings and videos10min
1 exercício prático
Short-time Fourier transform20min
Semana
55 horas para concluir
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 vídeos (total de (Total 115 mín.) min), 1 leitura, 2 testes
8 videos
Sinusoidal model 115min
Sinusoidal model 213min
Sinusoidal model 317min
Sinusoidal model13min
Analyzing a sound12min
Peak detection14min
Sinusoidal synthesis12min
sineModel16min
1 leituras
Advance reading10min
1 exercício prático
Sinusoidal model20min
Semana
65 horas para concluir
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 vídeos (total de (Total 120 mín.) min), 1 leitura, 2 testes
7 videos
Harmonic model13min
F0 detection20min
Pitch detection14min
Harmonic model25min
Analyzing a sound14min
F0 detection16min
harmonicModel14min
1 leituras
Advanced readings10min
1 exercício prático
Harmonic model20min
Semana
75 horas para concluir
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 vídeos (total de (Total 126 mín.) min), 1 leitura, 2 testes
8 videos
Stochastic model20min
Sinusoidal plus residual modeling16min
Stochastic model10min
Harmonic plus residual model14min
Harmonic plus stochastic model12min
stochasticModel17min
hprModel19min
hpsModel14min
1 leituras
Advanced readings10min
1 exercício prático
Sinusoidal plus residual model20min
Semana
85 horas para concluir
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 vídeos (total de (Total 120 mín.) min), 1 leitura, 2 testes
9 videos
Sounds transformations 216min
Morphing with STFT10min
Time scaling11min
Pitch changes12min
Morphing with HPS12min
stftTransformations18min
sineTransformations11min
hpsTransformations9min
1 leituras
Advanced readings10min
1 exercício prático
Sound transformations20min
Semana
95 horas para concluir
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 vídeos (total de (Total 142 mín.) min), 2 testes
6 videos
Audio features30min
Sound and music description24min
Sound descriptors14min
Freesound20min
Intro to Essentia25min
Freesound API26min
1 exercício prático
Sound and music description20min
Semana
102 horas para concluir
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 vídeos (total de (Total 106 mín.) min), 1 leitura, 1 teste
6 videos
Review12min
MTG-UPF18min
Goodbye17min
Dunya18min
AcousticBrainz22min
1 leituras
Advanced readings10min
1 exercício prático
Concluding topics20min
6 horas para concluir
Concluding topics: Lesson Choices
3 testes
67%
consegui um benefício significativo de carreira com este curso
Melhores avaliações
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!
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.
Instrutores
Xavier Serra
Full ProfessorDept. of Information and Communication Technologies, UPF
Prof Julius O Smith, III
Professor of Music and (by courtesy) Electrical EngineeringCCRMA
Sobre Universidade Pompeu Fabra de 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.
Sobre Universidade 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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
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.
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