Audio Signal Processing for Music Applications

Go to Course: https://www.coursera.org/learn/audio-signal-processing

Introduction

**Course Review: Audio Signal Processing for Music Applications on Coursera** --- **Overview** If you're passionate about music and technology, the "Audio Signal Processing for Music Applications" course on Coursera is an exceptional opportunity to delve into the methodology of audio processing, specifically tailored for musical contexts. Taught by experts in the field, this course offers a robust combination of theoretical knowledge and practical applications, focusing on spectral processing techniques essential for analyzing, synthesizing, transforming, and describing audio signals in music. The course is well-structured and leverages open-source software to provide hands-on experience, ensuring that you not only learn the concepts but also implement them using programming tools like Python. --- **Detailed Review** * **Syllabus Overview** The course unfolds in a logical progression, starting with an introduction to both the field of Audio Signal Processing and the fundamental mathematics necessary for this discipline. It then gradually introduces various advanced topics including the Discrete Fourier Transform (DFT), Short-Time Fourier Transform (STFT), and different sound models (sinusoidal, harmonic, and stochastic). 1. **Introduction**: Here, you’ll get familiarized with the course structure and the Python programming environment, particularly the sms-tools package, which is central to practical applications throughout the course. 2. **Discrete Fourier Transform**: This section lays a strong foundation as you will learn the DFT's pivotal role in signal analysis, including how to use it practically on real-world sound samples obtained from platforms like Freesound.org. 3. **Fourier Theorems and STFT**: You will explore the essential theorems governing Fourier analysis and delve into the Short-Time Fourier Transform. The course includes practical demonstrations that help understand these concepts better through visual analysis of sounds using spectrograms. 4. **Sound Models (Sinusoidal, Harmonic, Stochastic)**: Each model focuses on unique types of sound signal representations and manipulations. You will engage in practical exercises that require coding in Python, enhancing your technical skills and understanding of audio signals. 5. **Sound Transformations**: This section is particularly exciting, as it involves applying your knowledge to real-time sound processing tasks like filtering, morphing, and time-frequency scaling – key techniques used in music production. 6. **Sound and Music Description**: Discover how to extract audio features and classify sounds. This is a crucial skill in music analysis, and the course guides you step-by-step in using advanced tools and APIs for your explorations. 7. **Concluding Topics**: As the course wraps up, you will get insights into continuing your learning journey and exploring additional resources. The presentation of innovative tools like Dunya and AcousticBrainz adds depth to your understanding of audio processing applications in the music industry. --- **Practical Feedback** The course’s integration of programming with real-world applications is a standout feature. The hands-on projects not only reinforce your learning but provide you with a portfolio of skills that can be applied in music technology and beyond. The use of the sms-tools package offers a practical environment for experimentation, and the ability to utilize Freesound.org for sound samples lends a contemporary relevance to the exercises. The instructional design maintains engagement with a balanced mix of theory and practice, bolstering your understanding of how the theoretical concepts translate into real-world scenarios. Additionally, the community aspect of using forums to discuss technical queries enhances the learning experience, creating a collaborative learning environment. --- **Recommendation** I wholeheartedly recommend the "Audio Signal Processing for Music Applications" course for anyone with an interest in music technology, audio engineering, or sound design. Whether you are a complete beginner or someone with a background in music wanting to enhance your technical skills, this course will provide essential knowledge and practical experience. By the end of this course, not only will you have a solid foundation in audio signal processing, but you will also be equipped with skills to manipulate and analyze sounds effectively for various applications in the music industry. Dive into the course and prepare to unlock a new realm of sound understanding and creativity!

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.

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.

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.

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 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 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.

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.

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.

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.

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.