publications | Music Informatics Group

2023

Lerch, Alexander

An Introduction to Audio Content Analysis: Music Information Retrieval Tasks and Applications Book

2, Wiley-IEEE Press, Hoboken, N.J, 2023, ISBN: 978-1-119-89094-2.

Abstract | Links | BibTeX | Tags: analysis, audio, Audio content analysis, audio signal processing, Automatic Music Transcription, Computer sound processing, machine listening, Matlab, MIR, music analysis, music informatics, music information retrieval, Python

@book{lerch_introduction_2023,

title = {An Introduction to Audio Content Analysis: Music Information Retrieval Tasks and Applications},

author = {Alexander Lerch},

url = {https://ieeexplore.ieee.org/servlet/opac?bknumber=9965970},

isbn = {978-1-119-89094-2},

year  = {2023},

date = {2023-01-01},

urldate = {2022-01-01},

publisher = {Wiley-IEEE Press},

address = {Hoboken, N.J},

edition = {2},

abstract = {An Introduction to Audio Content Analysis Enables readers to understand the algorithmic analysis of musical audio signals with AI-driven approaches An Introduction to Audio Content Analysis 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. It gives readers the algorithmic understanding to teach a computer to interpret music signals and thus allows for 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. A multitude of audio content analysis tasks related to the extraction of tonal, temporal, timbral, and intensity-related characteristics of the music signal are presented. Each task is introduced from both a musical and a technical perspective, detailing the algorithmic approach as well as providing practical guidance on implementation details and evaluation. To aid in reader comprehension, each task description begins with a short introduction to the most important musical and perceptual characteristics of the covered topic, followed by a detailed algorithmic model and its evaluation, 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: 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 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 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 amount of references.},

keywords = {analysis, audio, Audio content analysis, audio signal processing, Automatic Music Transcription, Computer sound processing, machine listening, Matlab, MIR, music analysis, music informatics, music information retrieval, Python},

pubstate = {published},

tppubtype = {book}

}

An Introduction to Audio Content Analysis Enables readers to understand the algorithmic analysis of musical audio signals with AI-driven approaches An Introduction to Audio Content Analysis 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. It gives readers the algorithmic understanding to teach a computer to interpret music signals and thus allows for 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. A multitude of audio content analysis tasks related to the extraction of tonal, temporal, timbral, and intensity-related characteristics of the music signal are presented. Each task is introduced from both a musical and a technical perspective, detailing the algorithmic approach as well as providing practical guidance on implementation details and evaluation. To aid in reader comprehension, each task description begins with a short introduction to the most important musical and perceptual characteristics of the covered topic, followed by a detailed algorithmic model and its evaluation, 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: 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 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 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 amount of references.