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 Wu, Chih-Wei; Lerch, Alexander Learned Features for the Assessment of Percussive Music Performances Proceedings Article In: Proceedings of the International Conference on Semantic Computing (ICSC), IEEE, Laguna Hills, 2018. Links | BibTeX | Tags: audio, feature learning, music performance analysis, percussion Lerch, Alexander An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics Book Wiley-IEEE Press, Hoboken, 2012, ISBN: 978-1-118-26682-3. Abstract | Links | BibTeX | Tags: analysis, audio, audio signal processing, information, listening, machine, machine listening, music, music analysis, music information retrieval, processing, retrieval, signal Lerch, Alexander Software-Based Extraction of Objective Parameters from Music Performances Book GRIN Verlag, München, 2009, ISBN: 978-3-640-29496-1. Abstract | Links | BibTeX | Tags: analysis, audio, content, information, music, performance, retrieval Yogev, Noam; Lerch, Alexander A System for Automatic Audio Harmonization ( Ein System für automatische Audio-Harmonisierung) Proceedings Article In: Proceedings of the VdT International Convention (25. Tonmeistertagung), Leipzig, 2008. Abstract | Links | BibTeX | Tags: audio, harmonization2023
@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}
}
2018
@inproceedings{wu_learned_2018,
title = {Learned Features for the Assessment of Percussive Music Performances},
author = {Chih-Wei Wu and Alexander Lerch},
url = {http://www.musicinformatics.gatech.edu/wp-content_nondefault/uploads/2018/01/Wu_Lerch_2018_Learned-Features-for-the-Assessment-of-Percussive-Music-Performances.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the International Conference on Semantic Computing (ICSC)},
publisher = {IEEE},
address = {Laguna Hills},
keywords = {audio, feature learning, music performance analysis, percussion},
pubstate = {published},
tppubtype = {inproceedings}
}
2012
@book{lerch_introduction_2012,
title = {An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics},
author = {Alexander Lerch},
url = {http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6266785},
isbn = {978-1-118-26682-3},
year = {2012},
date = {2012-01-01},
publisher = {Wiley-IEEE Press},
address = {Hoboken},
abstract = {With the proliferation of digital audio distribution over digital media, audio content analysis is fast becoming a requirement for designers of intelligent signal-adaptive audio processing systems. Written by a well-known expert in the field, this book provides quick access to different analysis algorithms and allows comparison between different approaches to the same task, making it useful for newcomers to audio signal processing and industry experts alike. A review of relevant fundamentals in audio signal processing, psychoacoustics, and music theory, as well as downloadable MATLAB files are also included. Please visit the companion website: www.AudioContentAnalysis.org},
keywords = {analysis, audio, audio signal processing, information, listening, machine, machine listening, music, music analysis, music information retrieval, processing, retrieval, signal},
pubstate = {published},
tppubtype = {book}
}
2009
@book{lerch_software-based_2009,
title = {Software-Based Extraction of Objective Parameters from Music Performances},
author = {Alexander Lerch},
url = {http://dx.doi.org/10.14279/depositonce-2025},
isbn = {978-3-640-29496-1},
year = {2009},
date = {2009-01-01},
publisher = {GRIN Verlag},
address = {M\"{u}nchen},
abstract = {Different music performances of the same score may significantly differ from each other. It is obvious that not only the composer’s work, the score, defines the listener’s music experience, but that the music performance itself is an integral part of this experience. Music performers use the information contained in the score, but interpret, transform or add to this information. Four parameter classes can be used to describe a performance objectively: tempo and timing, loudness, timbre and pitch. Each class contains a multitude of individual parameters that are at the performers’ disposal to generate a unique physical rendition of musical ideas. The extraction of such objective parameters is one of the difficulties in music performance research. This work presents an approach to the software-based extraction of tempo and timing, loudness and timbre parameters from audio files to provide a tool for the automatic parameter extraction from music performances. The system is applied to extract data from 21 string quartet performances and a detailed analysis of the extracted data is presented. The main contributions of this thesis are the adaptation and development of signal processing approaches to performance parameter extraction and the presentation and discussion of string quartet performances of a movement of Beethoven’s late String Quartet op. 130.},
keywords = {analysis, audio, content, information, music, performance, retrieval},
pubstate = {published},
tppubtype = {book}
}
2008
@inproceedings{yogev_system_2008,
title = {A System for Automatic Audio Harmonization ( Ein System f\"{u}r automatische Audio-Harmonisierung)},
author = {Noam Yogev and Alexander Lerch},
url = {http://www.musicinformatics.gatech.edu/wp-content_nondefault/uploads/2016/10/Yogev-and-Lerch-2008-A-System-for-Automatic-Audio-Harmonization-Ein-S-1.pdf},
doi = {10.1.1.148.8391},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings of the VdT International Convention (25. Tonmeistertagung)},
address = {Leipzig},
abstract = {A rule-based system for automatic melody harmonization is presented. It models the cognitive process a human arranger undergoes when confronted with the same task, namely: segmenting the melody into phrases, tagging melody notes with harmonic functions, establishing a palette of possible chords for each note, and finding the most agreeable voicing through these chords. The system is designed to be embedded in an audio framework, which synthe- sizes a four-voiced audio output using pitch-shifting techniques. Principles of classical counterpoint as well as common voice-leading conven- tions are utilized by the system. We shall outline the various phases of computa- tion, describe the rules applied in each phase, and present perspectives regarding the stylistic flexibility suggested by the system's design.},
keywords = {audio, harmonization},
pubstate = {published},
tppubtype = {inproceedings}
}
publications
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. Learned Features for the Assessment of Percussive Music Performances Proceedings Article In: Proceedings of the International Conference on Semantic Computing (ICSC), IEEE, Laguna Hills, 2018. An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics Book Wiley-IEEE Press, Hoboken, 2012, ISBN: 978-1-118-26682-3. Software-Based Extraction of Objective Parameters from Music Performances Book GRIN Verlag, München, 2009, ISBN: 978-3-640-29496-1. A System for Automatic Audio Harmonization ( Ein System für automatische Audio-Harmonisierung) Proceedings Article In: Proceedings of the VdT International Convention (25. Tonmeistertagung), Leipzig, 2008.2023
2018
2012
2009
2008