An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics
With the proliferation of digital audio distribution over digital media, audio content analysis is fast becoming a requirement for designers of intelligent signal-adaptive audio processing systems. Written by a well-known expert in the field, this book provides quick access to different analysis algorithms and allows comparison between different approaches to the same task, making it useful for newcomers to audio signal processing and industry experts alike. A review of relevant fundamentals in audio signal processing, psychoacoustics, and music theory, as well as downloadable MATLAB files are also included. Please visit the companion website: www.AudioContentAnalysis.org
Why Read This Book
You will get a concise, practical tour of audio content analysis that connects DSP fundamentals, psychoacoustics, and music informatics to real algorithms you can implement and compare. The book emphasizes hands-on MATLAB examples and side-by-side discussions of alternative approaches so you can quickly prototype feature extraction, classification, and retrieval workflows.
Who Will Benefit
Engineers, graduate students, and industry practitioners with some DSP background who need a practical reference for audio feature extraction, music-information-retrieval tasks, and prototyping algorithms in MATLAB.
Level: Intermediate — Prerequisites: Basic signals and systems / digital signal processing, linear algebra and basic probability/statistics; familiarity with MATLAB (or Octave) will substantially speed practical work.
Key Takeaways
- Implement common audio features and descriptors (e.g., MFCCs, chroma, spectral contrast, spectral centroid)
- Apply time–frequency transforms (FFT, STFT, spectrograms, and wavelets) and use them for spectral analysis
- Design and analyze digital filters and perceptually motivated feature pipelines for audio and speech
- Prototype and evaluate algorithms for pitch detection, onset/beat detection, and audio fingerprinting using downloadable MATLAB code
- Use statistical and machine-learning methods to build classifiers, segmenters, and retrieval systems for music-informatics tasks
Topics Covered
- 1. Introduction to Audio Content Analysis and Applications
- 2. Fundamentals of Digital Audio and Signal Processing
- 3. Psychoacoustics and Basic Music Theory for Analysis
- 4. Time–Frequency Representations: FFT, STFT, and Wavelets
- 5. Spectral Features and Descriptors
- 6. Temporal and Rhythm Features (onsets, beats, tempo)
- 7. Tonal and Pitch-Related Features (chroma, pitch tracking)
- 8. Perceptual Feature Design and Filter Techniques
- 9. Statistical Signal Processing and Pattern Recognition for Audio
- 10. Music Information Retrieval Tasks: Classification, Retrieval, Segmentation
- 11. Evaluation, Datasets, and Performance Measures
- 12. MATLAB Examples, Implementation Notes, and Companion Resources
Languages, Platforms & Tools
How It Compares
Compared with Meinard Müller's Fundamentals of Music Processing (more algorithmic/theoretical depth on MIR) and Giannakopoulos & Pikrakis's Introduction to Audio Analysis (more focus on machine-learning pipelines), Lerch's book is more of a compact, MATLAB-oriented practical reference emphasizing algorithm comparison and implementation.
