Enables readers to understand the analysis of musical audio signals through AI-driven applications in music education and production
An Introduction to Audio Content Analysis serves as a comprehensive guide on audio content analysis and how to apply it in signal processing and music informatics, allowing readers to teach a computer to interpret music signals and thus allowing the design of tools for interacting with music. The work ties together topics from audio signal processing and machine learning, showing how to use audio content analysis to pick up musical characteristics automatically. The analysis of audio signals and the extraction of metadata describing the content of the signal are both clearly explained, covering both abstract descriptions of technical properties and musical descriptions such as tempo, harmony and key, musical style, and performance attributes. Musical information, such as tonal, pitch, harmony, key, temporal, and tempo, is given a separate analysis in each category.
To aid in reader comprehension, each chapter begins with a short introduction to the most important musical and perceptual characteristics of the covered topic, followed by a detailed algorithmic model and concluded with questions and exercises. For the interested reader, updated supplemental materials are provided via an accompanying website.
Written by a well-known expert in the music industry, sample topics covered in Introduction to Audio Content Analysis include:
- Input representation: periodic signals, random signals, statistical signal descriptions (arithmetic mean, geometric mean, harmonic mean, and more), digital audio signals, and block-based processing
- Tonal analysis: human perception of pitch, representation of pitch in music, fundamental frequency detection, and monophonic and polyphonic input signals
- Intensity: representation of dynamics in music, root mean square, peak envelope, and psycho-acoustic loudness features
- Temporal analysis: representation of temporal events in music, onset detection, beat histograms, and detection of tempo and beat phase
- Alignment: dynamic time warping, audio-to-audio alignment, and audio-to-score alignment
An invaluable guide for newcomers to audio signal processing and industry experts alike, An Introduction to Audio Content Analysis covers a wide range of introductory topics pertaining to machine listening, allowing students of and researchers in audio analysis to quickly gain core holistic knowledge and dig deeper into specific aspects of the field.
About the Author:
Alexander Lerch, PhD, is an Associate Professor at the Center for Music Technology, Georgia Institute of Technology. His research focuses on Signal Processing and Machine Learning applied to Music, an interdisciplinary field commonly referred to as Music Information Retrieval (MIR). He has authored more than 50 peer-reviewed publications and his website, www.AudioContentAnalysis.org, is a popular resource on Audio Content Analysis, providing video lectures, code examples, and other materials.
