Об этом курсе: 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.
Для кого этот курс: This course is primary aimed at advanced undergraduate or master students, along with professionals, interested in signal processing, programming and music.
Автор: Universitat Pompeu Fabra of Barcelona, Stanford University
Преподаватели: Xavier Serra, Full Professor
Dept. of Information and Communication Technologies, UPF
Преподаватели: Prof Julius O Smith, III, Professor of Music and (by courtesy) Electrical Engineering
CCRMA
| Уровень | Учащийся среднего уровня |
| Выполнение | 10 weeks of study, 8 hours/week |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
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 видео, 1 материал для самостоятельного изучения
- Видео: Welcome
- Видео: Introduction to Audio Signal Processing
- Видео: Course outline
- Видео: Basic mathematics
- Видео: Introduction to Audacity
- Видео: Introduction to SonicVisualizer
- Видео: Introduction to sms-tools
- Видео: Introduction to Python
- Видео: Python and sounds
- Видео: sms-tools software
- Reading: Advanced readings and videos
Оцениваемый: Basics
Оцениваемый: Python and sound
НЕДЕЛЯ 2
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 видео, 1 материал для самостоятельного изучения
- Видео: DFT 2
- Видео: Analyzing a sound
- Видео: Introduction to Freesound
- Видео: Sinusoids
- Видео: DFT
- Reading: Advanced readings and videos
Оцениваемый: DFT
Оцениваемый: Sinusoids and DFT
НЕДЕЛЯ 3
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 видео, 1 материал для самостоятельного изучения
- Видео: Fourier properties 2
- Видео: Periodic signals
- Видео: Complex sounds
- Видео: Spectrum
- Видео: Fourier properties
- Видео: dftModel
- Reading: Advanced readings and videos
Оцениваемый: Fourier properties
Оцениваемый: Fourier Properties
НЕДЕЛЯ 4
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 видео, 1 материал для самостоятельного изучения
- Видео: STFT 2
- Видео: Spectrogram
- Видео: Analyzing a sound
- Видео: Windows
- Видео: STFT
- Reading: Advanced readings and videos
Оцениваемый: Short-time Fourier transform
Оцениваемый: Short-time Fourier Transform (STFT)
НЕДЕЛЯ 5
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 видео, 1 материал для самостоятельного изучения
- Видео: Sinusoidal model 2
- Видео: Sinusoidal model 3
- Видео: Sinusoidal model
- Видео: Analyzing a sound
- Видео: Peak detection
- Видео: Sinusoidal synthesis
- Видео: sineModel
- Reading: Advance reading
Оцениваемый: Sinusoidal model
Оцениваемый: Sinusoidal model
НЕДЕЛЯ 6
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 видео, 1 материал для самостоятельного изучения
- Видео: F0 detection
- Видео: Pitch detection
- Видео: Harmonic model
- Видео: Analyzing a sound
- Видео: F0 detection
- Видео: harmonicModel
- Reading: Advanced readings
Оцениваемый: Harmonic model
Оцениваемый: Harmonic Model
НЕДЕЛЯ 7
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 видео, 1 материал для самостоятельного изучения
- Видео: Sinusoidal plus residual modeling
- Видео: Stochastic model
- Видео: Harmonic plus residual model
- Видео: Harmonic plus stochastic model
- Видео: stochasticModel
- Видео: hprModel
- Видео: hpsModel
- Reading: Advanced readings
Оцениваемый: Sinusoidal plus residual model
Оцениваемый: Sinusoidal plus residual
НЕДЕЛЯ 8
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 видео, 1 материал для самостоятельного изучения
- Видео: Sounds transformations 2
- Видео: Morphing with STFT
- Видео: Time scaling
- Видео: Pitch changes
- Видео: Morphing with HPS
- Видео: stftTransformations
- Видео: sineTransformations
- Видео: hpsTransformations
- Reading: Advanced readings
Оцениваемый: Sound transformations
Оцениваемый: Transformations
НЕДЕЛЯ 9
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 видео
- Видео: Sound and music description
- Видео: Sound descriptors
- Видео: Freesound
- Видео: Intro to Essentia
- Видео: Freesound API
Оцениваемый: Sound and music description
Оцениваемый: Sound and music description
НЕДЕЛЯ 10
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 видео, 1 материал для самостоятельного изучения
- Видео: Review
- Видео: MTG-UPF
- Видео: Goodbye
- Видео: Dunya
- Видео: AcousticBrainz
- Reading: Advanced readings
Оцениваемый: Concluding topics
Оцениваемый: A music piece combining sounds and their transformations
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Авторы
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. UPF’s MOOCs are produced within this general goal.
Stanford University
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.
Рейтинги и отзывы
An excellent course, particularily when enhanced by reading the four book series by Juli us O. Smith III.
Allowing one to continue into the next semester while keeping credit for work done is a major
plus point for the course. It actually took me about a year to complete and the alloted ten weeks would be an extremely demanding schedule.
This course is amazing I wish I can give 6 stars
best course to apply your math knowledment
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Excellent course to understand DSP, Fourier Transform and Audio Programming.