About this course: 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.
Who is this class for: This course is primary aimed at advanced undergraduate or master students, along with professionals, interested in signal processing, programming and music.
Created by: Universitat Pompeu Fabra of Barcelona, Stanford University
Taught by: Xavier Serra, Associate Professor
Dept. of Information and Communication Technologies, UPF
Taught by: Prof Julius O Smith, III, Professor of Music and (by courtesy) Electrical Engineering
CCRMA
| Level | Intermediate |
| Commitment | 10 weeks of study, 8 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 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 videos, 1 reading
- Video: Teaser
- Video: Welcome
- Video: Introduction to Audio Signal Processing
- Video: Course outline
- Video: Basic mathematics
- Video: Introduction to Audacity
- Video: Introduction to SonicVisualizer
- Video: Introduction to sms-tools
- Video: Introduction to Python
- Video: Python and sounds
- Video: sms-tools software
- Reading: Advanced readings and videos
Graded: Basics
Graded: Python and sound
WEEK 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 videos, 1 reading
- Video: DFT 1
- Video: DFT 2
- Video: Analyzing a sound
- Video: Introduction to Freesound
- Video: Sinusoids
- Video: DFT
- Reading: Advanced readings and videos
Graded: DFT
Graded: Sinusoids and DFT
WEEK 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 videos, 1 reading
- Video: Fourier properties 1
- Video: Fourier properties 2
- Video: Periodic signals
- Video: Complex sounds
- Video: Spectrum
- Video: Fourier properties
- Video: dftModel
- Reading: Advanced readings and videos
Graded: Fourier properties
Graded: Fourier Properties
WEEK 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 videos, 1 reading
- Video: STFT 1
- Video: STFT 2
- Video: Spectrogram
- Video: Analyzing a sound
- Video: Windows
- Video: STFT
- Reading: Advanced readings and videos
Graded: Short-time Fourier transform
Graded: Short-time Fourier Transform (STFT)
WEEK 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 videos, 1 reading
- Video: Sinusoidal model 1
- Video: Sinusoidal model 2
- Video: Sinusoidal model 3
- Video: Sinusoidal model
- Video: Analyzing a sound
- Video: Peak detection
- Video: Sinusoidal synthesis
- Video: sineModel
- Reading: Advance reading
Graded: Sinusoidal model
Graded: Sinusoidal model
WEEK 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 videos, 1 reading
- Video: Harmonic model
- Video: F0 detection
- Video: Pitch detection
- Video: Harmonic model
- Video: Analyzing a sound
- Video: F0 detection
- Video: harmonicModel
- Reading: Advanced readings
Graded: Harmonic model
Graded: Harmonic Model
WEEK 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 videos, 1 reading
- Video: Stochastic model
- Video: Sinusoidal plus residual modeling
- Video: Stochastic model
- Video: Harmonic plus residual model
- Video: Harmonic plus stochastic model
- Video: stochasticModel
- Video: hprModel
- Video: hpsModel
- Reading: Advanced readings
Graded: Sinusoidal plus residual model
Graded: Sinusoidal plus residual
WEEK 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 videos, 1 reading
- Video: Sounds transformations 1
- Video: Sounds transformations 2
- Video: Morphing with STFT
- Video: Time scaling
- Video: Pitch changes
- Video: Morphing with HPS
- Video: stftTransformations
- Video: sineTransformations
- Video: hpsTransformations
- Reading: Advanced readings
Graded: Sound transformations
Graded: Transformations
WEEK 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 videos
- Video: Audio features
- Video: Sound and music description
- Video: Sound descriptors
- Video: Freesound
- Video: Intro to Essentia
- Video: Freesound API
Graded: Sound and music description
Graded: Sound and music description
WEEK 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 videos, 1 reading
- Video: Beyond audio processing
- Video: Review
- Video: MTG-UPF
- Video: Goodbye
- Video: Dunya
- Video: AcousticBrainz
- Reading: Advanced readings
Graded: Concluding topics
Graded: A music piece combining sounds and their transformations
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Creators
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.
Ratings and Reviews
Excellent
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.
Great Course, lots of concepts to learn and implement! I had amazing time doing the assignments. Should be an ASPMA part 2 course.
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Lots of learning in 10 weeks. Teacher is good takes time and explain things well. Lectures include theory, demonstration and programming which helped me learn the basics really well. Thank you !