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.
Обработка аудиосигнала при создании музыкальных произведений
от партнера
Университет Помпеу Фабра, Барселона
Стэнфордский университет
Университет Помпеу Фабра, Барселона
Об этом курсе
4.8
Оценки: 202
Приобретаемые навыки
Digital Signal ProcessingSignal ProcessingPython ProgrammingFft Algorithms
Программа курса: что вы изучите
Неделя
16 ч. на завершение
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 ((всего 126 мин.)), 1 материал для самостоятельного изучения, 2 тестов
11 видео
Teaser3мин
Welcome4мин
Introduction to Audio Signal Processing13мин
Course outline10мин
Basic mathematics16мин
Introduction to Audacity9мин
Introduction to SonicVisualizer10мин
Introduction to sms-tools17мин
Introduction to Python11мин
Python and sounds13мин
sms-tools software14мин
1 материал для самостоятельного изучения
Advanced readings and videos10мин
1 практическое упражнение
Basics20мин
Неделя
25 ч. на завершение
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 ((всего 78 мин.)), 1 материал для самостоятельного изучения, 2 тестов
6 видео
DFT 111мин
DFT 216мин
Analyzing a sound8мин
Introduction to Freesound12мин
Sinusoids14мин
DFT15мин
1 материал для самостоятельного изучения
Advanced readings and videos10мин
1 практическое упражнение
DFT20мин
Неделя
35 ч. на завершение
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 ((всего 99 мин.)), 1 материал для самостоятельного изучения, 2 тестов
7 видео
Fourier properties 114мин
Fourier properties 213мин
Periodic signals11мин
Complex sounds9мин
Spectrum13мин
Fourier properties23мин
dftModel13мин
1 материал для самостоятельного изучения
Advanced readings and videos10мин
1 практическое упражнение
Fourier properties20мин
Неделя
45 ч. на завершение
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 ((всего 90 мин.)), 1 материал для самостоятельного изучения, 2 тестов
1 материал для самостоятельного изучения
Advanced readings and videos10мин
1 практическое упражнение
Short-time Fourier transform20мин
4.8
Рецензии: 5567%
получил значимые преимущества в карьере благодаря этому курсу
Лучшие рецензии
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.
Преподаватели
Xavier Serra
Full ProfessorDept. of Information and Communication Technologies, UPF
Prof Julius O Smith, III
Professor of Music and (by courtesy) Electrical EngineeringCCRMA
О Университет Помпеу Фабра, Барселона
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.
О Стэнфордский университет
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.
Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
Зарегистрировавшись на сертификацию, вы получите доступ ко всем видео, тестам и заданиям по программированию (если они предусмотрены). Задания по взаимной оценке сокурсниками можно сдавать и проверять только после начала сессии. Если вы проходите курс без оплаты, некоторые задания могут быть недоступны.
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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