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.
Offered By
Universitat Pompeu Fabra of Barcelona
Stanford University
Audio Signal Processing for Music Applications
Universitat Pompeu Fabra of BarcelonaAbout this Course
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Learner Career Outcomes
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Start instantly and learn at your own schedule.
Flexible deadlines
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Intermediate Level
Approx. 54 hours to complete
English
Subtitles: English
Skills you will gain
Digital Signal ProcessingSignal ProcessingPython ProgrammingFft Algorithms
Learner Career Outcomes
67%
got a tangible career benefit from this course
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 54 hours to complete
English
Subtitles: English
Offered by
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.
Syllabus - What you will learn from this course
6 hours to complete
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 hours to complete
11 videos (Total 126 min), 1 reading, 2 quizzes
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 reading
Advanced readings and videos10m
1 practice exercise
Basics20m
5 hours to complete
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 hours to complete
6 videos (Total 78 min), 1 reading, 2 quizzes
1 reading
Advanced readings and videos10m
1 practice exercise
DFT20m
5 hours to complete
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 hours to complete
7 videos (Total 99 min), 1 reading, 2 quizzes
7 videos
Fourier properties 213m
Periodic signals11m
Complex sounds9m
Spectrum13m
Fourier properties23m
dftModel13m
1 reading
Advanced readings and videos10m
1 practice exercise
Fourier properties20m
5 hours to complete
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 hours to complete
6 videos (Total 90 min), 1 reading, 2 quizzes
1 reading
Advanced readings and videos10m
1 practice exercise
Short-time Fourier transform20m
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
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.
More questions? Visit the Learner Help Center.
