Introduction to Audio Analysis: A MATLAB® Approach
Introduction to Audio Analysis serves as a standalone introduction to audio analysis, providing theoretical background to many state-of-the-art techniques. It covers the essential theory necessary to develop audio engineering applications, but also uses programming techniques, notably MATLAB®, to take a more applied approach to the topic. Basic theory and reproducible experiments are combined to demonstrate theoretical concepts from a practical point of view and provide a solid foundation in the field of audio analysis.
Audio feature extraction, audio classification, audio segmentation, and music information retrieval are all addressed in detail, along with material on basic audio processing and frequency domain representations and filtering. Throughout the text, reproducible MATLAB® examples are accompanied by theoretical descriptions, illustrating how concepts and equations can be applied to the development of audio analysis systems and components. A blend of reproducible MATLAB® code and essential theory provides enable the reader to delve into the world of audio signals and develop real-world audio applications in various domains.
- Practical approach to signal processing: The first book to focus on audio analysis from a signal processing perspective, demonstrating practical implementation alongside theoretical concepts
- Bridge the gap between theory and practice: The authors demonstrate how to apply equations to real-life code examples and resources, giving you the technical skills to develop real-world applications
- Library of MATLAB code: The book is accompanied by a well-documented library of MATLAB functions and reproducible experiments
Why Read This Book
You will get a practical, hands-on introduction to audio analysis that ties essential DSP and statistical techniques to reproducible MATLAB experiments. The book teaches the building blocks for real-world audio tasks (feature extraction, segmentation, classification, MIR) while explaining the underlying theory so you can both prototype and reason about results.
Who Will Benefit
Graduate students, audio engineers, and DSP practitioners who want to build and evaluate audio analysis systems using MATLAB and need a balanced mix of theory and applied examples.
Level: Intermediate — Prerequisites: Basic digital signal processing (Fourier transforms, sampling), linear algebra, and familiarity with MATLAB programming.
Key Takeaways
- Extract common audio features (e.g., spectral descriptors, MFCCs, chroma) from audio signals using reproducible MATLAB code.
- Apply time-frequency analysis (STFT, spectrograms) and basic filtering to prepare audio for higher-level tasks.
- Implement and evaluate audio segmentation and onset detection algorithms for music and general audio.
- Train and evaluate classifiers for audio tagging and music information retrieval using practical datasets and performance metrics.
- Use practical MATLAB toolchains and workflows to prototype and benchmark audio-analysis pipelines.
Topics Covered
- 1. Introduction to Audio Analysis and MATLAB workflows
- 2. Basics of sound, sampling and time-domain representations
- 3. Fourier analysis and spectral representations
- 4. Short-Time Fourier Transform and time-frequency analysis
- 5. Audio preprocessing and filtering
- 6. Low-level audio feature extraction (spectral descriptors, energy, zero-crossing)
- 7. Perceptual and music-related features (MFCCs, chroma, tonnetz)
- 8. Statistical descriptions and feature post-processing
- 9. Classification techniques for audio (GMM, SVM, k-NN, basic ML pipelines)
- 10. Audio segmentation, onset detection and event detection
- 11. Music Information Retrieval applications (genre, tempo, chord estimation)
- 12. Evaluation, datasets and experimental protocols
- 13. MATLAB examples, reproducible experiments and appendices
Languages, Platforms & Tools
How It Compares
Covers similar applied ground to Alexander Lerch's Introduction to Audio Content Analysis but is more MATLAB-oriented and hands-on; Meinard Müller's Fundamentals of Music Processing is more music-theory centric and mathematically formal.
