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.
ofrecido por
Universitat Pompeu Fabra of Barcelona
Universidad de Stanford
Audio Signal Processing for Music Applications
Universitat Pompeu Fabra of Barcelona
Acerca de este Curso
65,995 vistas recientes
Resultados profesionales del estudiante
67%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Habilidades que obtendrás
Digital Signal ProcessingSignal ProcessingPython ProgrammingFft Algorithms
Resultados profesionales del estudiante
67%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 53 horas para completar
Sugerido: 10 weeks of study, 8 hours/week
Inglés (English)
Subtítulos: Inglés (English)
Programa - Qué aprenderás en este curso
6 horas para completar
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.
6 horas para completar
11 videos (Total 126 minutos), 1 lectura, 2 cuestionarios
11 videos
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
1 lectura
Advanced readings and videos10m
1 ejercicio de práctica
Basics20m
5 horas para completar
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.
5 horas para completar
6 videos (Total 78 minutos), 1 lectura, 2 cuestionarios
1 lectura
Advanced readings and videos10m
1 ejercicio de práctica
DFT20m
5 horas para completar
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.
5 horas para completar
7 videos (Total 99 minutos), 1 lectura, 2 cuestionarios
7 videos
Fourier properties 213m
Periodic signals11m
Complex sounds9m
Spectrum13m
Fourier properties23m
dftModel13m
1 lectura
Advanced readings and videos10m
1 ejercicio de práctica
Fourier properties20m
5 horas para completar
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.
5 horas para completar
6 videos (Total 90 minutos), 1 lectura, 2 cuestionarios
1 lectura
Advanced readings and videos10m
1 ejercicio de práctica
Short-time Fourier transform20m
Revisiones
Principales revisiones sobre AUDIO SIGNAL PROCESSING FOR MUSIC APPLICATIONS
por LNDec 3, 2016
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!
por EODec 8, 2019
An absolutely awesome introduction to Audio Signal Processing. The additive introduction of new concepts is capable of teaching any beginner this topic which ordinarily is difficult to understand.
por HZJan 20, 2017
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.
por DAMar 6, 2019
Assignments are easily doable without any application of real analysis skills. You guys should actually ask us to build certain parts of your models so that we achieve that level of skill.
por TBMar 7, 2018
Good lectures with a focus on practical applications. Good introduction to how signal processing can be used for musical analysis, and more specifically how to use the Essentia library
por DAJun 6, 2017
Excellent material for an interesting subject. The video lectures are very good. The course does need some updates for Python3 and associated libraries to stay current.
por FRDec 3, 2016
Very well explained and organized course material. The classes are also very detailed and special emphasis is put on illustrating every concept with example plots.
por PSJun 4, 2017
Excellent course, for me this was pitched at just the right level to be interesting and also challenging. Everything was well explained and well structured.
por AAMar 4, 2017
good course . If there will any Speech recognition course it will useful > Hope Coursera will start speech recognition course in future .
por GIJul 10, 2019
Thank you kindly! The course is quite useful. I understood not only mathematics of Fourier Transform, but also the meaning of each step.
por MEMar 5, 2018
It's a wonderful course when you can begin in the world of Digital Audio Processing. Very recommended! Thank you very much!
por UDDec 17, 2016
I have just finished the week three and I'm enjoying every part of the course. Thank you, thank you and thank you again.
ofrecido por
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.
Universidad de Stanford
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.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
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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