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Audio Signal Processing for Music Apps
Master audio signal processing for music. Learn spectral techniques to analyze, synthesize, and transform sounds using Python and open-source tools.
Course Cost
Free course
Intermediate
Skill Level
43 Hours
Self-paced lessons
This course teaches audio signal processing methodologies specific to music and real applications. It focuses on spectral processing techniques for analyzing, synthesizing, transforming, and describing audio signals in music contexts. The curriculum covers the Discrete Fourier Transform, Fourier theorems, Short-time Fourier transform, and various models including sinusoidal, harmonic, and sinusoidal plus residual. Students learn to implement these concepts using Python and open-source tools. The course also explores sound transformations, audio feature extraction, and music description techniques. It emphasizes practical applications and includes demonstrations of various software tools and programming exercises.
57,172 Enrolled
English
What you'll learn
Understand and implement the Discrete Fourier Transform (DFT) for audio signal analysis
Master the Short-time Fourier Transform (STFT) and its applications in spectrograms
Develop proficiency in sinusoidal and harmonic modeling of audio signals
Learn stochastic modeling and residual analysis techniques
Implement various sound transformation methods using different models
Gain skills in audio feature extraction and music description
Apply Python programming to audio signal processing tasks
Understand the practical applications of audio signal processing in music technology
Skills you'll gain
This course includes:
13.5 Hours PreRecorded video
10 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 11 modules in this course
This course provides a comprehensive introduction to audio signal processing for music applications. It covers fundamental concepts such as the Discrete Fourier Transform, Fourier theorems, and Short-time Fourier transform, as well as advanced topics like sinusoidal, harmonic, and residual models. Students learn to implement these concepts using Python and open-source tools, gaining practical experience in analyzing, synthesizing, and transforming audio signals. The course also explores sound transformations, audio feature extraction, and music description techniques, providing a solid foundation for various music technology applications. Throughout the modules, students engage with real-world examples and hands-on programming exercises, preparing them for advanced work in music technology and audio signal processing.
Introduction
Module 1 · 5 Hours to complete
Discrete Fourier transform
Module 2 · 4 Hours to complete
Fourier theorems
Module 3 · 5 Hours to complete
Short-time Fourier transform
Module 4 · 5 Hours to complete
Sinusoidal model
Module 5 · 5 Hours to complete
Harmonic model
Module 6 · 5 Hours to complete
Sinusoidal plus residual model
Module 7 · 3 Hours to complete
Sound transformations
Module 8 · 3 Hours to complete
Sound and music description
Module 9 · 3 Hours to complete
Concluding topics
Module 10 · 2 Hours to complete
Concluding topics: Lesson Choices
Module 11 · 3 Hours to complete
Fee Structure
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
4.9
37
Reviews
57,624
Students
1
Course
4.9
37
Reviews
57,264
Students
1
Course
Frequently asked Questions
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