Audio content analysis (ACA) is actually a subtopic of the broader music information retrieval (MIR) research area. This subtopic deals with bringing out musical and perceptual properties directly from the audio signals to improve human-computer interaction (HCI) with digital audio signals. A good understanding of ACA assists in the design of intelligent MIR applications and content-adaptive audio processing systems. In the author’s own words, “ACA is a multidisciplinary research field” requiring knowledge from “different research fields such as musicology and music theory, (music) psychology, psychoacoustics, audio engineering, library science, and last but not least computer science for pattern recognition and machine learning.”

Chapter 1 introduces ACA and chapter 2 covers the fundamentals of audio signals and signal processing. The major topics covered in the remaining chapters of the book include instantaneous features (such as statistical properties, spectral shape, and signal properties), intensity, tonal analysis, temporal analysis, alignment, musical genre, similarity and mood, audio fingerprinting, and music performance analysis. The author provides a very handy appendix on convolution properties, Fourier transforms, principal component analysis, and software for audio analysis.

The book includes many salient features:

• It is a very good guide to ACA and its application in signal processing and music informatics.
• It treats various characteristics of musical information separately, including pitch, harmony, tempo, key, tonality, and timbre.
• It includes a helpful review of the basics of audio signal processing, music theory, and psychoacoustics (making it useful as an introductory text).
• It analyzes and compares different algorithms for the same task.
• Its companion website (http://www.audiocontentanalysis.org/) includes invaluable MATLAB programs that are freely downloadable.
• It concludes with a comprehensive bibliography.

The author is an acknowledged expert in the music industry. This book will not only greatly help undergraduate and graduate ACA students, but will also be a boon to music researchers and music industry experts alike.

The book is simply a treasure for music analysts, and I would strongly recommend it for any scientific library. It does not, however, focus on speech signals; as such, automatic speech recognition, although within the scope of ACA, has been omitted. To use the book profitably, an elementary knowledge of digital signal processing (DSP) is necessary.

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