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; Dittmar, Christian; Southall, Carl; Vogl, Richard; Widmer, Gerhard; Hockman, Jason A; Muller, Meinard; Lerch, Alexander A Review of Automatic Drum Transcription Journal Article In: IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 9, pp. 1457–1483, 2018, ISSN: 2329-9290. Abstract | Links | BibTeX | Tags: Automatic Music Transcription, deep learning, Instruments, Machine Learning, Matrix Factorization, Rhythm, Spectrogram, Speech processing, Task analysis, Transient analysis2023
@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
@article{wu_review_2018,
title = {A Review of Automatic Drum Transcription},
author = {Chih-Wei Wu and Christian Dittmar and Carl Southall and Richard Vogl and Gerhard Widmer and Jason A Hockman and Meinard Muller and Alexander Lerch},
url = {http://www.musicinformatics.gatech.edu/wp-content_nondefault/uploads/2018/05/Wu-et-al.-2018-A-review-of-automatic-drum-transcription.pdf},
doi = {10.1109/TASLP.2018.2830113},
issn = {2329-9290},
year = {2018},
date = {2018-01-01},
journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
volume = {26},
number = {9},
pages = {1457--1483},
abstract = {In Western popular music, drums and percussion are an important means to emphasize and shape the rhythm, often defining the musical style. If computers were able to analyze the drum part in recorded music, it would enable a variety of rhythm-related music processing tasks. Especially the detection and classification of drum sound events by computational methods is considered to be an important and challenging research problem in the broader field of Music Information Retrieval. Over the last two decades, several authors have attempted to tackle this problem under the umbrella term Automatic Drum Transcription (ADT). This paper presents a comprehensive review of ADT research, including a thorough discussion of the task-specific challenges, categorization of existing techniques, and evaluation of several state-of-the-art systems. To provide more insights on the practice of ADT systems, we focus on two families of ADT techniques, namely methods based on Non-negative Matrix Factorization and Recurrent Neural Networks. We explain the methods' technical details and drum-specific variations and evaluate these approaches on publicly available datasets with a consistent experimental setup. Finally, the open issues and under-explored areas in ADT research are identified and discussed, providing future directions in this field.},
keywords = {Automatic Music Transcription, deep learning, Instruments, Machine Learning, Matrix Factorization, Rhythm, Spectrogram, Speech processing, Task analysis, Transient analysis},
pubstate = {published},
tppubtype = {article}
}
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. A Review of Automatic Drum Transcription Journal Article In: IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 9, pp. 1457–1483, 2018, ISSN: 2329-9290.2023
2018