Audio Signal Processing for Music Applications from Coursera

What's inside

Syllabus

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Concluding topics: Lesson Choices

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Taught by recognized instructors in this field: Xavier Serra, Prof Julius O Smith, III

Develops core skills in spectral processing techniques

Uses freely available software and materials

Focuses on music related applications of audio signal processing

Introduces students to relevant open software libraries such as Essentia and STOMP

Provides interactive materials with demonstrations of various signal processing techniques

Reviews summary

Audio signal processing for music enthusiasts

Learners say this engaging course provides a comprehensive introduction to audio signal processing. It particularly benefits those with a background in Python programming, mathematics, or music. Students appreciate the clear explanations of theory and the hands-on exercises that solidify concepts. While the programming assignments can be challenging, they are an opportunity for significant learning. Overall, it's a rewarding course for those interested in pursuing computational musicology.

Course introduces current research in computational musicology.

"It also introduces you to...current research in computational musicology."

Programming assignments provide a steep learning curve.

"Assignments were challenging, but they make for a great learning experience."

Practical exercises reinforce theoretical concepts.

"A lot of excercies and practices that helps you to solidify the concepts."

"It makes Python programming approachable (basic previous knowledge is needed) with hands-on exercises."

Theory lectures provide easy-to-understand explanations.

"Excellent course with great explanations of the theory..."

"The lectures walk you through the theory, the application..."

Assumes background knowledge in Python, mathematics, and possibly music.

"As the course page mentions, this is an intermediate level course."

"It doesn't cover filter design and other signal processing topics in detail."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Audio Signal Processing for Music Applications with these activities:

Review fundamental concepts of mathematics used in audio signal processing

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Refreshing your knowledge of the underlying mathematics will strengthen your foundation for understanding audio signal processing concepts.

Browse courses on Mathematics

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Review textbooks or online resources on relevant mathematical concepts
Solve practice problems to reinforce your understanding

Explore online tutorials on harmonic models for audio analysis

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Following tutorials on harmonic models will expand your knowledge of advanced techniques for analyzing and manipulating audio signals.

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Search for online tutorials on harmonic models for audio analysis
Select reputable tutorials and follow their instructions carefully
Practice using the techniques demonstrated in the tutorials

Create a visual representation of the Discrete Fourier Transform (DFT) process

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Creating a visual representation of the DFT process will reinforce your understanding of its steps and how it transforms signals.

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Review the steps of the DFT process
Choose a visual format, such as a flowchart, diagram, or animation
Create your visual representation, clearly illustrating each step of the DFT process

Four other activities

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Solve practice problems on Fourier theorems

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Practice solving problems related to Fourier theorems will enhance your understanding of their properties and applications.

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Gather practice problems on Fourier theorems
Attempt to solve the problems independently
Review your solutions and identify areas for improvement

Develop a presentation on a specific application of audio signal processing

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Creating a presentation on a practical application will help you synthesize your knowledge and communicate it effectively.

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Choose a specific application of audio signal processing
Research and gather information on the topic
Develop a clear and engaging presentation outline
Create slides and prepare visual aids
Rehearse and deliver your presentation

Develop a simple audio filter using the Short-Time Fourier Transform (STFT)

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Building an audio filter using the STFT will provide hands-on experience in applying spectral analysis techniques to practical applications.

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Research and understand the principles of the STFT
Choose a programming language and library for audio processing
Implement an STFT-based filter algorithm
Test and refine your filter on different audio samples

Contribute to open-source projects related to audio signal processing

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Contributing to open-source projects will provide you with practical experience and a deeper understanding of real-world applications of audio signal processing techniques.

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Identify open-source projects related to audio signal processing
Review their documentation and codebase
Identify areas where you can contribute
Make contributions to the project, such as bug fixes, feature enhancements, or documentation improvements

Career center

Learners who complete Audio Signal Processing for Music Applications will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use data to solve problems and make predictions. This course may be useful for aspiring Data Scientists by providing them with a foundation in audio signal processing techniques, including the discrete Fourier transform, Fourier theorems, and short-time Fourier transform. They will also learn about sinusoidal and harmonic models, as well as sound transformations and sound and music description, all of which are relevant to the work of a Data Scientist in the field of audio signal processing.

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Software Engineer

Software Engineers design, develop, and maintain software systems. This course may be useful for aspiring Software Engineers by providing them with a foundation in audio signal processing techniques, including the discrete Fourier transform, Fourier theorems, and short-time Fourier transform. They will also learn about sinusoidal and harmonic models, as well as sound transformations and sound and music description, all of which are relevant to the work of a Software Engineer in the field of audio signal processing.

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Professor

Professors teach and conduct research at colleges and universities. This course may be useful for aspiring Professors by providing them with a foundation in audio signal processing techniques, including the discrete Fourier transform, Fourier theorems, and short-time Fourier transform. They will also learn about sinusoidal and harmonic models, as well as sound transformations and sound and music description, all of which are relevant to the work of a Professor in the field of audio signal processing.

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Speech-Language Pathologist

Speech-Language Pathologists assess and treat speech, language, and swallowing disorders. This course may be useful for aspiring Speech-Language Pathologists by providing them with a foundation in audio signal processing techniques, including the discrete Fourier transform, Fourier theorems, and short-time Fourier transform. They will also learn about sinusoidal and harmonic models, as well as sound transformations and sound and music description, all of which are relevant to the work of a Speech-Language Pathologist.

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Audio Engineer

As an Audio Engineer, you will be responsible for recording, mixing, and mastering audio for a variety of purposes, such as music, film, and television. This course may help you develop your skills as an Audio Engineer by providing you with a foundation in audio signal processing techniques, including the discrete Fourier transform, Fourier theorems, and short-time Fourier transform. You will also learn about sinusoidal and harmonic models, as well as sound transformations and sound and music description, all of which are relevant to the work of an Audio Engineer.

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Researcher

Researchers conduct scientific research in a variety of fields, including audio signal processing. This course may be useful for aspiring Researchers by providing them with a foundation in audio signal processing techniques, including the discrete Fourier transform, Fourier theorems, and short-time Fourier transform. They will also learn about sinusoidal and harmonic models, as well as sound transformations and sound and music description, all of which are relevant to the work of a Researcher in the field of audio signal processing.

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Acoustical Engineer

Acoustical Engineers use their knowledge of sound and vibration to design and improve the acoustic environment in buildings, vehicles, and other spaces. This course may be useful for aspiring Acoustical Engineers by providing them with a foundation in audio signal processing techniques, including the discrete Fourier transform, Fourier theorems, and short-time Fourier transform. They will also learn about sinusoidal and harmonic models, as well as sound transformations and sound and music description, all of which are relevant to the work of an Acoustical Engineer.

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Audiologist

Audiologists assess and treat hearing and balance disorders. This course may be useful for aspiring Audiologists by providing them with a foundation in audio signal processing techniques, including the discrete Fourier transform, Fourier theorems, and short-time Fourier transform. They will also learn about sinusoidal and harmonic models, as well as sound transformations and sound and music description, all of which are relevant to the work of an Audiologist.

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Musician

Musicians create, perform, and teach music. This course may be useful for aspiring Musicians by providing them with a foundation in audio signal processing techniques, including the discrete Fourier transform, Fourier theorems, and short-time Fourier transform. They will also learn about sinusoidal and harmonic models, as well as sound transformations and sound and music description, all of which are relevant to the work of a Musician.

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Sound Designer

In the role of a Sound Designer, you will be responsible for creating, editing and mixing sound effects and music for film, television, radio, video games and other media. An understanding of audio signal processing is essential for a Sound Designer, and this course may help you build that understanding by teaching you the relevant spectral processing techniques used to describe and transform sounds. It also covers the sinusoidal and harmonic models used in sound synthesis, as well as sound transformations and sound and music description, all of which are relevant to the work of a Sound Designer.

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Music Producer

Music Producers are responsible for overseeing all aspects of music production, from recording and editing to mixing and mastering. This course may be useful for aspiring Music Producers by providing them with a foundation in audio signal processing techniques, including the discrete Fourier transform, Fourier theorems, and short-time Fourier transform. They will also learn about sinusoidal and harmonic models, as well as sound transformations and sound and music description, all of which are relevant to the work of a Music Producer.

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Music Therapist

Music Therapists use music to help people improve their physical, emotional, and mental health. This course may be useful for aspiring Music Therapists by providing them with a foundation in audio signal processing techniques, including the discrete Fourier transform, Fourier theorems, and short-time Fourier transform. They will also learn about sinusoidal and harmonic models, as well as sound transformations and sound and music description, all of which are relevant to the work of a Music Therapist.

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Composer

Composers create original music. This course may be useful for aspiring Composers by providing them with a foundation in audio signal processing techniques, including the discrete Fourier transform, Fourier theorems, and short-time Fourier transform. They will also learn about sinusoidal and harmonic models, as well as sound transformations and sound and music description, all of which are relevant to the work of a Composer.

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Music Teacher

Music Teachers teach music to students of all ages. This course may be useful for aspiring Music Teachers by providing them with a foundation in audio signal processing techniques, including the discrete Fourier transform, Fourier theorems, and short-time Fourier transform. They will also learn about sinusoidal and harmonic models, as well as sound transformations and sound and music description, all of which are relevant to the work of a Music Teacher.

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Music Technologist

Music Technologists use their knowledge of music, technology, and acoustics to design, develop, and maintain music technology systems. This course may be useful for aspiring Music Technologists by providing them with a foundation in audio signal processing techniques, including the discrete Fourier transform, Fourier theorems, and short-time Fourier transform. They will also learn about sinusoidal and harmonic models, as well as sound transformations and sound and music description, all of which are relevant to the work of a Music Technologist.

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