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.
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ポンペウ・ファブラ大学（Universitat Pompeu Fabra of Barcelona）
スタンフォード大学（Stanford University）
ポンペウ・ファブラ大学（Universitat Pompeu Fabra of Barcelona）
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このコースについて
27,462 最近の表示
習得するスキル
Digital Signal ProcessingSignal ProcessingPython ProgrammingFft Algorithms
シラバス - 本コースの学習内容
週
16時間で修了
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件のビデオ （合計126分）, 1 reading, 2 quizzes
11件のビデオ
Teaser3 分
Welcome4 分
Introduction to Audio Signal Processing13 分
Course outline10 分
Basic mathematics16 分
Introduction to Audacity9 分
Introduction to SonicVisualizer10 分
Introduction to sms-tools17 分
Introduction to Python11 分
Python and sounds13 分
sms-tools software14 分
1件の学習用教材
Advanced readings and videos10 分
1の練習問題
Basics20 分
週
25時間で修了
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件のビデオ （合計78分）, 1 reading, 2 quizzes
1件の学習用教材
Advanced readings and videos10 分
1の練習問題
DFT20 分
週
35時間で修了
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件のビデオ （合計99分）, 1 reading, 2 quizzes
7件のビデオ
Fourier properties 213 分
Periodic signals11 分
Complex sounds9 分
Spectrum13 分
Fourier properties23 分
dftModel13 分
1件の学習用教材
Advanced readings and videos10 分
1の練習問題
Fourier properties20 分
週
45時間で修了
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件のビデオ （合計90分）, 1 reading, 2 quizzes
1件の学習用教材
Advanced readings and videos10 分
1の練習問題
Short-time Fourier transform20 分
週
55時間で修了
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件のビデオ （合計115分）, 1 reading, 2 quizzes
8件のビデオ
Sinusoidal model 213 分
Sinusoidal model 317 分
Sinusoidal model13 分
Analyzing a sound12 分
Peak detection14 分
Sinusoidal synthesis12 分
sineModel16 分
1件の学習用教材
Advance reading10 分
1の練習問題
Sinusoidal model20 分
週
65時間で修了
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件のビデオ （合計120分）, 1 reading, 2 quizzes
7件のビデオ
Harmonic model13 分
F0 detection20 分
Pitch detection14 分
Harmonic model25 分
Analyzing a sound14 分
F0 detection16 分
harmonicModel14 分
1件の学習用教材
Advanced readings10 分
1の練習問題
Harmonic model20 分
週
75時間で修了
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件のビデオ （合計126分）, 1 reading, 2 quizzes
8件のビデオ
Stochastic model20 分
Sinusoidal plus residual modeling16 分
Stochastic model10 分
Harmonic plus residual model14 分
Harmonic plus stochastic model12 分
stochasticModel17 分
hprModel19 分
hpsModel14 分
1件の学習用教材
Advanced readings10 分
1の練習問題
Sinusoidal plus residual model20 分
週
85時間で修了
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件のビデオ （合計120分）, 1 reading, 2 quizzes
9件のビデオ
Sounds transformations 216 分
Morphing with STFT10 分
Time scaling11 分
Pitch changes12 分
Morphing with HPS12 分
stftTransformations18 分
sineTransformations11 分
hpsTransformations9 分
1件の学習用教材
Advanced readings10 分
1の練習問題
Sound transformations20 分
週
95時間で修了
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件のビデオ （合計142分）, 2 quizzes
6件のビデオ
Audio features30 分
Sound and music description24 分
Sound descriptors14 分
Freesound20 分
Intro to Essentia25 分
Freesound API26 分
1の練習問題
Sound and music description20 分
週
102時間で修了
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件のビデオ （合計106分）, 1 reading, 1 quiz
1件の学習用教材
Advanced readings10 分
1の練習問題
Concluding topics20 分
6時間で修了
Concluding topics: Lesson Choices
3 quizzes
4.8
56件のレビュー67%
コースが具体的なキャリアアップにつながった
人気のレビュー
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.
講師
Xavier Serra
Full ProfessorDept. of Information and Communication Technologies, UPF
Prof Julius O Smith, III
Professor of Music and (by courtesy) Electrical EngineeringCCRMA
ポンペウ・ファブラ大学（Universitat Pompeu Fabra of Barcelona）について
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.
スタンフォード大学（Stanford University）について
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.
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いつ講座や課題にアクセスできるようになりますか？
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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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