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Introduction to Audio Analysis

Book Description

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

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Preface
  6. Acknowledgments
  7. List of Tables
  8. List of figures
  9. 1: Basic Concepts, Representations and Feature Extraction
    1. 1: Introduction
      1. 1.1 The MATLAB Audio Analysis Library
      2. 1.2 Outline of Chapters
      3. 1.3 A Note on Exercises
    2. 2: Getting Familiar with Audio Signals
      1. 2.1 Sampling
      2. 2.2 Playback
      3. 2.3 Mono and Stereo Audio Signals
      4. 2.4 Reading and Writing Audio Files
      5. 2.5 Reading Audio Files in Blocks
      6. 2.6 Recording Audio Data
      7. 2.7 Short-term Audio Processing
      8. 2.8 Exercises
    3. 3: Signal Transforms and Filtering Essentials
      1. 3.1 The Discrete Fourier Transform
      2. 3.2 The Short-Time Fourier Transform
      3. 3.3 Aliasing in More Detail
      4. 3.4 The Discrete Cosine Transform
      5. 3.5 The Discrete-Time Wavelet Transform
      6. 3.6 Digital Filtering Essentials
      7. 3.7 Digital Filters in MATLAB
      8. 3.8 Exercises
    4. 4: Audio Features
      1. 4.1 Short-Term and Mid-Term Processing
      2. 4.2 Class Definitions
      3. 4.3 Time-Domain Audio Features
      4. 4.4 Frequency-Domain Audio Features
      5. 4.5 Periodicity Estimation and Harmonic Ratio
      6. 4.6 Exercises
  10. 2: Audio Content Characterization
    1. 5: Audio Classification
      1. 5.1 Classification Fundamentals
      2. 5.2 Popular Classifiers
      3. 5.3 Implementation-Related Issues
      4. 5.4 Evaluation
      5. 5.5 Case Studies
      6. 5.6 Exercises
    2. 6: Audio Segmentation
      1. 6.1 Segmentation with Embedded Classification
      2. 6.2 Segmentation Without Classification
      3. 6.3 Exercises
    3. 7: Audio Alignment and Temporal Modeling
      1. 7.1 Audio Sequence Alignment
      2. 7.2 Hidden Markov Modeling
      3. 7.3 The Viterbi Algorithm
      4. 7.4 The Baum-Welch Algorithm
      5. 7.5 HMM Training
      6. 7.6 Exercises
  11. 3: Other Issues
    1. 8: Music Information Retrieval
      1. 8.1 Music Thumbnailing
      2. 8.2 Music Meter and Tempo Induction
      3. 8.3 Music Content Visualization
      4. 8.4 Exercises
    2. Appendix A: The Matlab Audio Analysis Library
      1. 1 Supplementary data
      2. 2 Supplementary data
    3. Appendix B: Audio-Related Libraries and Software
      1. B.1 MATLAB
      2. B.2 Python
      3. B.3 C/C++
    4. Appendix C: Audio Datasets
  12. Bibliography
  13. Index