Universitat Pompeu Fabra of Barcelona
Audio Signal Processing for Music Applications
Universitat Pompeu Fabra of Barcelona

Audio Signal Processing for Music Applications

Taught in English

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56,538 already enrolled

Course

Gain insight into a topic and learn the fundamentals

Xavier Serra
Prof Julius O Smith, III

Instructors: Xavier Serra

4.8

(286 reviews)

Intermediate level
Some related experience required
49 hours to complete
3 weeks at 16 hours a week
Flexible schedule
Learn at your own pace

Details to know

Assessments

10 quizzes

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There are 11 modules in this course

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.

What's included

11 videos1 reading1 quiz1 programming assignment

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.

What's included

6 videos1 reading1 quiz1 programming assignment

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.

What's included

7 videos1 reading1 quiz1 programming assignment

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.

What's included

6 videos1 reading1 quiz1 programming assignment

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.

What's included

8 videos1 reading1 quiz1 programming assignment

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.

What's included

7 videos1 reading1 quiz1 programming assignment

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.

What's included

8 videos1 reading1 quiz1 peer review

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.

What's included

9 videos1 reading1 quiz1 peer review

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.

What's included

6 videos1 quiz1 peer review

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.

What's included

6 videos1 reading1 quiz

What's included

3 peer reviews

Instructors

Instructor ratings
4.9 (35 ratings)
Xavier Serra
Universitat Pompeu Fabra of Barcelona
1 Course56,538 learners
Prof Julius O Smith, III
Stanford University
1 Course56,538 learners

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4.8

286 reviews

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FE
4

Reviewed on Dec 16, 2018

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5

Reviewed on Jun 4, 2017

YZ
5

Reviewed on Dec 6, 2016

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