Lerch, Alexander Audioinhaltsanalyse Book Section In: Weinzierl, Stefan (Ed.): Handbuch der Audiotechnik, pp. 1–20, Springer Berlin Heidelberg, Berlin, Heidelberg, 2023, ISBN: 978-3-662-60357-4. Abstract | Links | BibTeX | Tags: Audio content analysis, Grundfrequenzerkennung, music information retrieval, Musikklassifizierung, Musiktranskription, Tonarterkennung 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 Lerch, Alexander libACA, pyACA, and ACA-Code: Audio Content Analysis in 3 Languages Journal Article In: Software Impacts, pp. 100349, 2022, ISSN: 2665-9638. Abstract | Links | BibTeX | Tags: Audio content analysis, C++, Matlab, music information retrieval, Python2023
@incollection{lerch_audioinhaltsanalyse_2023,
title = {Audioinhaltsanalyse},
author = {Alexander Lerch},
editor = {Stefan Weinzierl},
url = {https://doi.org/10.1007/978-3-662-60357-4_8-1},
doi = {10.1007/978-3-662-60357-4_8-1},
isbn = {978-3-662-60357-4},
year = {2023},
date = {2023-01-01},
urldate = {2023-03-30},
booktitle = {Handbuch der Audiotechnik},
pages = {1--20},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
abstract = {Audiosignale enthalten eine F\"{u}lle von Informationen, die sich Menschen beim H\"{o}ren leicht erschlie\ssen. So transportiert ein Sprachsignal nicht nur Informationen \"{u}ber den Text, sondern auch \"{u}ber den Sprecher (z. B. Geschlecht, Alter, Akzent) und die Aufnahmeumgebung (z. B. drinnen vs. drau\ssen). In einem Musiksignal k\"{o}nnen wir die Musikinstrumente, die musikalische Struktur, den Stil, die Melodie, Harmonien und Tonalit\"{a}t, einen emotionalen Ausdruck und andere Charakteristika der Darbietung sowie das K\"{o}nnen der Vortragenden identifizieren. Die Audioinhaltsanalyse (Audio Content Analysis, ACA) zielt darauf ab, Algorithmen zur automatischen Extraktion dieser Inhalte aus dem (digitalen) Audiosignal zu entwickeln und einzusetzen; diese Algorithmen erm\"{o}glichen es uns, das Audiosignal basierend auf dem Inhalt zu sortieren, zu kategorisieren, zu segmentieren und zu visualisieren (Lerch 2012). M\"{o}gliche Anwendungen sind inhaltsbasierte automatische Playlist-Generierung und Musikempfehlungssysteme, computergest\"{u}tzte Musikproduktion und -bearbeitung sowie intelligente Musiklernprogramme, die Musiksch\"{u}lerinnen und -sch\"{u}ler auf Fehler und Verbesserungsm\"{o}glichkeiten beim Instrumentalspiel hinweisen.},
keywords = {Audio content analysis, Grundfrequenzerkennung, music information retrieval, Musikklassifizierung, Musiktranskription, Tonarterkennung},
pubstate = {published},
tppubtype = {incollection}
}
@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}
}
2022
@article{lerch_libaca_2022-1,
title = {libACA, pyACA, and ACA-Code: Audio Content Analysis in 3 Languages},
author = {Alexander Lerch},
url = {https://www.sciencedirect.com/science/article/pii/S2665963822000677},
doi = {10.1016/j.simpa.2022.100349},
issn = {2665-9638},
year = {2022},
date = {2022-07-01},
urldate = {2022-07-04},
journal = {Software Impacts},
pages = {100349},
abstract = {The three packages libACA, pyACA, and ACA-Code provide reference implementations for basic approaches and algorithms for the analysis of musical audio signals in three different languages: C++, Python, and Matlab. All three packages cover the same algorithms, such as extraction of low level audio features, fundamental frequency estimation, as well as simple approaches to chord recognition, musical key detection, and onset detection. In addition, it implementations of more generic algorithms useful in audio content analysis such as dynamic time warping and the Viterbi algorithm are provided. The three packages thus provide a practical cross-language and cross-platform reference to students and engineers implementing audio analysis algorithms and enable implementation-focused learning of algorithms for audio content analysis and music information retrieval.},
keywords = {Audio content analysis, C++, Matlab, music information retrieval, Python},
pubstate = {published},
tppubtype = {article}
}
publications
Audioinhaltsanalyse Book Section In: Weinzierl, Stefan (Ed.): Handbuch der Audiotechnik, pp. 1–20, Springer Berlin Heidelberg, Berlin, Heidelberg, 2023, ISBN: 978-3-662-60357-4. 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. libACA, pyACA, and ACA-Code: Audio Content Analysis in 3 Languages Journal Article In: Software Impacts, pp. 100349, 2022, ISSN: 2665-9638.2023
2022