提供方

庞佩乌·法布拉·大学

斯坦福大学

音乐应用的音频信号处理

庞佩乌·法布拉·大学

課程信息

55,834 次近期查看

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.

學生職業成果

67%

通過此課程獲得實實在在的工作福利

100% 在線

立即開始，按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為54 小時

英語（English）

字幕：英語（English）

您將獲得的技能

Digital Signal ProcessingSignal ProcessingPython ProgrammingFft Algorithms

學生職業成果

67%

通過此課程獲得實實在在的工作福利

100% 在線

立即開始，按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為54 小時

英語（English）

字幕：英語（English）

講師

授課教師評分

5/5 （5 個評分）

Xavier Serra

Full Professor

Dept. of Information and Communication Technologies, UPF

36,899 個學生

1 門課程

Prof Julius O Smith, III

Professor of Music and (by courtesy) Electrical Engineering

CCRMA

36,899 個學生

1 門課程

提供方

庞佩乌·法布拉·大学

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.

斯坦福大学

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.

教學大綱 - 您將從這門課程中學到什麼

內容評分96%（5,536 個評分）

週

週 1

完成時間為 6 小時

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.

完成時間為 6 小時

11 個視頻（總計 126 分鐘）, 1 個閱讀材料, 2 個測驗

11 個視頻

Teaser3分鐘

Welcome4分鐘

Introduction to Audio Signal Processing13分鐘

Course outline10分鐘

Basic mathematics16分鐘

Introduction to Audacity9分鐘

Introduction to SonicVisualizer10分鐘

Introduction to sms-tools17分鐘

Introduction to Python11分鐘

Python and sounds13分鐘

sms-tools software14分鐘

1 個閱讀材料

Advanced readings and videos10分鐘

1 個練習

Basics20分鐘

週

週 2

完成時間為 5 小時

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.

完成時間為 5 小時

6 個視頻（總計 78 分鐘）, 1 個閱讀材料, 2 個測驗

6 個視頻

DFT 111分鐘

DFT 216分鐘

Analyzing a sound8分鐘

Introduction to Freesound12分鐘

Sinusoids14分鐘

DFT15分鐘

1 個閱讀材料

Advanced readings and videos10分鐘

1 個練習

DFT20分鐘

週

週 3

完成時間為 5 小時

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.

完成時間為 5 小時

7 個視頻（總計 99 分鐘）, 1 個閱讀材料, 2 個測驗

7 個視頻

Fourier properties 114分鐘

Fourier properties 213分鐘

Periodic signals11分鐘

Complex sounds9分鐘

Spectrum13分鐘

Fourier properties23分鐘

dftModel13分鐘

1 個閱讀材料

Advanced readings and videos10分鐘

1 個練習

Fourier properties20分鐘

週

週 4

完成時間為 5 小時

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.

完成時間為 5 小時

6 個視頻（總計 90 分鐘）, 1 個閱讀材料, 2 個測驗

6 個視頻

STFT 117分鐘

STFT 216分鐘

Spectrogram10分鐘

Analyzing a sound14分鐘

Windows16分鐘

STFT14分鐘

1 個閱讀材料

Advanced readings and videos10分鐘

1 個練習

Short-time Fourier transform20分鐘

週

週 5

完成時間為 5 小時

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.

完成時間為 5 小時

8 個視頻（總計 115 分鐘）, 1 個閱讀材料, 2 個測驗

8 個視頻

Sinusoidal model 115分鐘

Sinusoidal model 213分鐘

Sinusoidal model 317分鐘

Sinusoidal model13分鐘

Analyzing a sound12分鐘

Peak detection14分鐘

Sinusoidal synthesis12分鐘

sineModel16分鐘

1 個閱讀材料

Advance reading10分鐘

1 個練習

Sinusoidal model20分鐘

週

週 6

完成時間為 5 小時

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.

完成時間為 5 小時

7 個視頻（總計 120 分鐘）, 1 個閱讀材料, 2 個測驗

7 個視頻

Harmonic model13分鐘

F0 detection20分鐘

Pitch detection14分鐘

Harmonic model25分鐘

Analyzing a sound14分鐘

F0 detection16分鐘

harmonicModel14分鐘

1 個閱讀材料

Advanced readings10分鐘

1 個練習

Harmonic model20分鐘

週

週 7

完成時間為 5 小時

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.

完成時間為 5 小時

8 個視頻（總計 126 分鐘）, 1 個閱讀材料, 2 個測驗

8 個視頻

Stochastic model20分鐘

Sinusoidal plus residual modeling16分鐘

Stochastic model10分鐘

Harmonic plus residual model14分鐘

Harmonic plus stochastic model12分鐘

stochasticModel17分鐘

hprModel19分鐘

hpsModel14分鐘

1 個閱讀材料

Advanced readings10分鐘

1 個練習

Sinusoidal plus residual model20分鐘

週

週 8

完成時間為 5 小時

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.

完成時間為 5 小時

9 個視頻（總計 120 分鐘）, 1 個閱讀材料, 2 個測驗

9 個視頻

Sounds transformations 116分鐘

Sounds transformations 216分鐘

Morphing with STFT10分鐘

Time scaling11分鐘

Pitch changes12分鐘

Morphing with HPS12分鐘

stftTransformations18分鐘

sineTransformations11分鐘

hpsTransformations9分鐘

1 個閱讀材料

Advanced readings10分鐘

1 個練習

Sound transformations20分鐘

週

週 9

完成時間為 5 小時

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.

完成時間為 5 小時

6 個視頻（總計 142 分鐘）

6 個視頻

Audio features30分鐘

Sound and music description24分鐘

Sound descriptors14分鐘

Freesound20分鐘

Intro to Essentia25分鐘

Freesound API26分鐘

1 個練習

Sound and music description20分鐘

週

週 10

完成時間為 2 小時

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.

完成時間為 2 小時

6 個視頻（總計 106 分鐘）, 1 個閱讀材料, 1 個測驗

6 個視頻

Beyond audio processing16分鐘

Review12分鐘

MTG-UPF18分鐘

Goodbye17分鐘

Dunya18分鐘

AcousticBrainz22分鐘

1 個閱讀材料

Advanced readings10分鐘

1 個練習

Concluding topics20分鐘

完成時間為 6 小時

Concluding topics: Lesson Choices

完成時間為 6 小時

常見問題

我什么时候能够访问课程视频和作业？
注册以便获得证书后，您将有权访问所有视频、测验和编程作业（如果适用）。只有在您的班次开课之后，才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程，可能无法访问某些作业。
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.

還有其他問題嗎？請訪問學生幫助中心。