Fundamentals of Music Processing: Audio, Analysis, Algorithms, Applications
This textbook provides both profound technological knowledge and a comprehensive treatment of essential topics in music processing and music information retrieval. Including numerous examples, figures, and exercises, this book is suited for students, lecturers, and researchers working in audio engineering, computer science, multimedia, and musicology.
The book consists of eight chapters. The first two cover foundations of music representations and the Fourier transform―concepts that are then used throughout the book. In the subsequent chapters, concrete music processing tasks serve as a starting point. Each of these chapters is organized in a similar fashion and starts with a general description of the music processing scenario at hand before integrating it into a wider context. It then discusses―in a mathematically rigorous way―important techniques and algorithms that are generally applicable to a wide range of analysis, classification, and retrieval problems. At the same time, the techniques are directly applied to a specific music processing task. By mixing theory and practice, the book’s goal is to offer detailed technological insights as well as a deep understanding of music processing applications. Each chapter ends with a section that includes links to the research literature, suggestions for further reading, a list of references, and exercises. The chapters are organized in a modular fashion, thus offering lecturers and readers many ways to choose, rearrange or supplement the material. Accordingly, selected chapters or individual sections can easily be integrated into courses on general multimedia, information science, signal processing, music informatics, or the digital humanities.
Why Read This Book
You will get a rigorous, music-centered treatment of signal processing and music information retrieval that connects mathematical foundations to concrete algorithms and applications. The book balances theory (Fourier analysis, time–frequency methods, statistical techniques) with practical examples, exercises, and real-world music tasks so you can move from understanding to implementation.
Who Will Benefit
Graduate students, researchers, and engineers in audio engineering, computer science, and music technology who want to build or evaluate algorithms for music/audio analysis and retrieval.
Level: Intermediate — Prerequisites: Basic calculus and linear algebra, introductory digital signal processing (Fourier transform, convolution), and some programming experience (MATLAB or Python recommended); basic music-theory concepts are helpful but not required.
Key Takeaways
- Apply Fourier and short-time Fourier analysis to extract time–frequency representations used in music processing.
- Design and compute common spectral features (e.g., chroma, MFCC-like representations) for music and audio tasks.
- Implement pitch and melody extraction, beat/tempo detection, and rhythm analysis algorithms.
- Perform music structure analysis and segmentation using similarity matrices, novelty detection, and clustering methods.
- Use evaluation frameworks and datasets to benchmark music information retrieval systems.
- Integrate statistical and machine-learning approaches into music-processing pipelines (feature extraction → modeling → evaluation).
Topics Covered
- 1. Music Representations and Preliminaries
- 2. The Fourier Transform and Time–Frequency Analysis
- 3. Spectral and Temporal Features for Music
- 4. Pitch, Tuning, and Melody Extraction
- 5. Rhythm, Tempo, Beat Tracking, and Onset Detection
- 6. Music Structure Analysis and Segmentation
- 7. Harmony, Chord Recognition, and Transcription
- 8. Applications in Music Information Retrieval and Evaluation
- Appendices: Mathematical Background, Datasets, and Software Examples
- Exercises and Implementation Notes
Languages, Platforms & Tools
How It Compares
Compared to Alexander Lerch's 'Introduction to Audio Content Analysis', Müller's book is more music-focused and mathematically rigorous; unlike general DSP texts (e.g., Zölzer), it centers on music/MIR tasks and algorithmic pipelines.
