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.
Audio Signal Processing for Music Applications
ofrecido por
Universitat Pompeu Fabra of Barcelona
Universidad de Stanford
Universitat Pompeu Fabra of Barcelona
29,908 inscrito
Acerca de este Curso
27,891 vistas recientes
Habilidades que obtendrƔs
Digital Signal ProcessingSignal ProcessingPython ProgrammingFft Algorithms
Programa - QuƩ aprenderƔs en este curso
Semana
16 horas para completar
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 minutos), 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 lectura
Advanced readings and videos10m
1 ejercicio de prƔctica
Basics20m
Semana
25 horas para completar
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 minutos), 1 reading, 2 quizzes
1 lectura
Advanced readings and videos10m
1 ejercicio de prƔctica
DFT20m
Semana
35 horas para completar
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 minutos), 1 reading, 2 quizzes
7 videos
Fourier properties 213m
Periodic signals11m
Complex sounds9m
Spectrum13m
Fourier properties23m
dftModel13m
1 lectura
Advanced readings and videos10m
1 ejercicio de prƔctica
Fourier properties20m
Semana
45 horas para completar
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 minutos), 1 reading, 2 quizzes
1 lectura
Advanced readings and videos10m
1 ejercicio de prƔctica
Short-time Fourier transform20m
Semana
55 horas para completar
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 minutos), 1 reading, 2 quizzes
8 videos
Sinusoidal model 213m
Sinusoidal model 317m
Sinusoidal model13m
Analyzing a sound12m
Peak detection14m
Sinusoidal synthesis12m
sineModel16m
1 lectura
Advance reading10m
1 ejercicio de prƔctica
Sinusoidal model20m
Semana
65 horas para completar
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 minutos), 1 reading, 2 quizzes
7 videos
F0 detection20m
Pitch detection14m
Harmonic model25m
Analyzing a sound14m
F0 detection16m
harmonicModel14m
1 lectura
Advanced readings10m
1 ejercicio de prƔctica
Harmonic model20m
Semana
75 horas para completar
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 minutos), 1 reading, 2 quizzes
8 videos
Sinusoidal plus residual modeling16m
Stochastic model10m
Harmonic plus residual model14m
Harmonic plus stochastic model12m
stochasticModel17m
hprModel19m
hpsModel14m
1 lectura
Advanced readings10m
1 ejercicio de prƔctica
Sinusoidal plus residual model20m
Semana
85 horas para completar
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 minutos), 1 reading, 2 quizzes
9 videos
Sounds transformations 216m
Morphing with STFT10m
Time scaling11m
Pitch changes12m
Morphing with HPS12m
stftTransformations18m
sineTransformations11m
hpsTransformations9m
1 lectura
Advanced readings10m
1 ejercicio de prƔctica
Sound transformations20m
Semana
95 horas para completar
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 minutos), 2 quizzes
6 videos
Sound and music description24m
Sound descriptors14m
Freesound20m
Intro to Essentia25m
Freesound API26m
1 ejercicio de prƔctica
Sound and music description20m
Semana
102 horas para completar
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 minutos), 1 reading, 1 quiz
1 lectura
Advanced readings10m
1 ejercicio de prƔctica
Concluding topics20m
6 horas para completar
Concluding topics: Lesson Choices
3 quizzes
67%
consiguiĆ³ un beneficio tangible en su carrera profesional gracias a este curso
Principales revisiones
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.
Instructores
Xavier Serra
Full ProfessorDept. of Information and Communication Technologies, UPF
Prof Julius O Smith, III
Professor of Music and (by courtesy) Electrical EngineeringCCRMA
Acerca de 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. UPFās MOOCs are produced within this general goal.
Acerca de Universidad 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.
Preguntas Frecuentes
ĀæCuĆ”ndo podrĆ© acceder a las lecciones y tareas?
Una vez que te inscribes para obtener un Certificado, tendrĆ”s acceso a todos los videos, cuestionarios y tareas de programaciĆ³n (si corresponde). Las tareas calificadas por compaƱeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesiĆ³n. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
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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