The 2nd edition of An Introduction to Audio Content Analysis was released in 2023. Similar to the 1st edition, it introduces standard base-line approaches to a variety of audio analysis and music information retrieval tasks and provides the reader with a multitude of pointers and references if interested. The second edition of the book is restructured, modernized, and expanded and serves as a comprehensive guide on audio content analysis explaining how signal processing and machine learning approaches can be utilized for the extraction of musical content from audio.

The book itself is accompanied by a range of free resources for understanding, teaching, and developing audio analysis systems.

Sample topics covered in “Introduction to Audio Content Analysis” include:

• Digital audio signals and their representation, common time-frequency transforms, audio features
• Pitch and fundamental frequency detection, key and chord
• Representation of dynamics in music and intensity-related features
• Beat histograms, onset and tempo detection, beat histograms, and detection of structure in music, and sequence alignment
• Audio fingerprinting, musical genre, mood, and instrument classification

The book covers a wide range of introductory topics and tasks pertaining to music information retrieval and machine listening, allowing students and researchers to quickly gain core holistic knowledge in audio analysis and dig deeper into specific aspects of the field with the help of a large number of references. Unlike many tutorials and other resources, it is built on bottom-up approaches that can be developed from scratch without dependencies in order to facilitate a detailed understanding of concepts, algorithms, and technical challenges.

resources

In support not only of the text book itself but also of the wider (research) community, all accompanying resources are free, public, and open source.

source code

• Python implementations of core algorithms from the book
• C++ implementations of core algorithms from the book
• Matlab implementations of core algorithms from the book
• Matlab sources to generate all plots in the book

teaching & learning resources

• PDF slides modularized to match to the textbook structure
• LaTeX sources for slides
• lecture videos (for the 1st edition)

other resources

• list of datasets for music and audio tasks
• list of relevant conference deadlines

about the author

Alexander Lerch is Professor of Music Technology at the School of Music and Associate Dean for Research and Creative Practice at the College of Design, Georgia Institute of Technology. His research focuses on design and implementation of novel machine learning and signal processing approaches for music and audio.

Lerch studied Electrical Engineering at the Berlin Institute of Technology and Tonmeister (music production) at the University of Arts, Berlin; he received his PhD (Audio Communications) from the Berlin Institute of Technology in the year 2008. He co-founded the company zplane.development, an industry leader providing advanced music technology to the music industry. zplane technologies are nowadays used by millions of musicians and producers world-wide in a wide variety of products. In 2013, Lerch joined the Georgia Institute of Technology, where he leads the Music Informatics Group.

Lerch has published more than 80 peer-reviewed publications on a wide range of topics in audio and music analysis and processing. His widely used textbook “An Introduction to Audio Content Analysis” was published by Wiley/IEEE Press in 2012 (2nd edition 2023). Lerch had visiting appointments at the Central Conservatory of Music, Beijing, Shanghai Jiao Tong University, and ShanghaiTech University, and served as co-chair of ISMIR2021, the 22nd International Society for Music Information Retrieval Conference. He also serves as a Research Ambassador for the DAAD (German Academic Exchange Service), Board Member of the International Society for Music Information Retrieval, and on the IEEE SPS AASP Technical Committee.

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