무료

제공자:

바르셀로나 폼페우 파브라 대학교

스탠퍼드 대학교

Audio Signal Processing for Music Applications

바르셀로나 폼페우 파브라 대학교

이 강좌에 대하여

최근 조회 55,755회

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.

학습자 경력 결과

67%

가 이 강좌를 통해 확실한 경력상 이점을 얻음

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 54시간 필요

영어

자막: 영어

귀하가 습득할 기술

Digital Signal ProcessingSignal ProcessingPython ProgrammingFft Algorithms

학습자 경력 결과

67%

가 이 강좌를 통해 확실한 경력상 이점을 얻음

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 54시간 필요

영어

자막: 영어

강사

강사 평가

5/5 (5개의 평가)

Xavier Serra

Full Professor

Dept. of Information and Communication Technologies, UPF

37,021명의 학습자

1 Course

Prof Julius O Smith, III

Professor of Music and (by courtesy) Electrical Engineering

CCRMA

37,021명의 학습자

1 Course

제공자:

바르셀로나 폼페우 파브라 대학교

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.

스탠퍼드 대학교

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.

강의 계획 - 이 강좌에서 배울 내용

콘텐츠 평가96%(5,548개의 평가)

주

주 1

완료하는 데 6시간 필요

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.

완료하는 데 6시간 필요

11개 동영상 (총 126분), 1 개의 읽기 자료, 2 개의 테스트

11개의 동영상

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

1개의 읽기 자료

Advanced readings and videos10m

1개 연습문제

Basics20m

주

주 2

완료하는 데 5시간 필요

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.

완료하는 데 5시간 필요

6개 동영상 (총 78분), 1 개의 읽기 자료, 2 개의 테스트

6개의 동영상

DFT 111m

DFT 216m

Analyzing a sound8m

Introduction to Freesound12m

Sinusoids14m

DFT15m

1개의 읽기 자료

Advanced readings and videos10m

1개 연습문제

DFT20m

주

주 3

완료하는 데 5시간 필요

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.

완료하는 데 5시간 필요

7개 동영상 (총 99분), 1 개의 읽기 자료, 2 개의 테스트

7개의 동영상

Fourier properties 114m

Fourier properties 213m

Periodic signals11m

Complex sounds9m

Spectrum13m

Fourier properties23m

dftModel13m

1개의 읽기 자료

Advanced readings and videos10m

1개 연습문제

Fourier properties20m

주

주 4

완료하는 데 5시간 필요

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.

완료하는 데 5시간 필요

6개 동영상 (총 90분), 1 개의 읽기 자료, 2 개의 테스트

6개의 동영상

STFT 117m

STFT 216m

Spectrogram10m

Analyzing a sound14m

Windows16m

STFT14m

1개의 읽기 자료

Advanced readings and videos10m

1개 연습문제

Short-time Fourier transform20m

주

주 5

완료하는 데 5시간 필요

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.

완료하는 데 5시간 필요

8개 동영상 (총 115분), 1 개의 읽기 자료, 2 개의 테스트

8개의 동영상

Sinusoidal model 115m

Sinusoidal model 213m

Sinusoidal model 317m

Sinusoidal model13m

Analyzing a sound12m

Peak detection14m

Sinusoidal synthesis12m

sineModel16m

1개의 읽기 자료

Advance reading10m

1개 연습문제

Sinusoidal model20m

주

주 6

완료하는 데 5시간 필요

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.

완료하는 데 5시간 필요

7개 동영상 (총 120분), 1 개의 읽기 자료, 2 개의 테스트

7개의 동영상

Harmonic model13m

F0 detection20m

Pitch detection14m

Harmonic model25m

Analyzing a sound14m

F0 detection16m

harmonicModel14m

1개의 읽기 자료

Advanced readings10m

1개 연습문제

Harmonic model20m

주

주 7

완료하는 데 5시간 필요

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.

완료하는 데 5시간 필요

8개 동영상 (총 126분), 1 개의 읽기 자료, 2 개의 테스트

8개의 동영상

Stochastic model20m

Sinusoidal plus residual modeling16m

Stochastic model10m

Harmonic plus residual model14m

Harmonic plus stochastic model12m

stochasticModel17m

hprModel19m

hpsModel14m

1개의 읽기 자료

Advanced readings10m

1개 연습문제

Sinusoidal plus residual model20m

주

주 8

완료하는 데 5시간 필요

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.

완료하는 데 5시간 필요

9개 동영상 (총 120분), 1 개의 읽기 자료, 2 개의 테스트

9개의 동영상

Sounds transformations 116m

Sounds transformations 216m

Morphing with STFT10m

Time scaling11m

Pitch changes12m

Morphing with HPS12m

stftTransformations18m

sineTransformations11m

hpsTransformations9m

1개의 읽기 자료

Advanced readings10m

1개 연습문제

Sound transformations20m

주

주 9

완료하는 데 5시간 필요

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.

완료하는 데 5시간 필요

6개 동영상 (총 142분)

6개의 동영상

Audio features30m

Sound and music description24m

Sound descriptors14m

Freesound20m

Intro to Essentia25m

Freesound API26m

1개 연습문제

Sound and music description20m

주

주 10

완료하는 데 2시간 필요

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.

완료하는 데 2시간 필요

6개 동영상 (총 106분), 1 개의 읽기 자료, 1 개의 테스트

6개의 동영상

Beyond audio processing16m

Review12m

MTG-UPF18m

Goodbye17m

Dunya18m

AcousticBrainz22m

1개의 읽기 자료

Advanced readings10m

1개 연습문제

Concluding topics20m

완료하는 데 6시간 필요

Concluding topics: Lesson Choices

완료하는 데 6시간 필요

자주 묻는 질문

강의 및 과제를 언제 이용할 수 있게 되나요?
강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.
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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