An review of automatic drum transcription

Wu, Chih-Wei and Dittmar, Christian and Southall, Carl and Vogl, Richard and Widmer, Gerhard and Hockman, Jason and Muller, Meinard and Lerch, Alexander (2018) An review of automatic drum transcription. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26 (9). pp. 1457-1483. ISSN 2329-9290

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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 Nonnegative 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

Item Type: Article
Additional Information: © 2018 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Uncontrolled Keywords: Music Information Retrieval, Automatic Music Transcription, Automatic Drum Transcription, Machine Learning, Matrix Factorization, Deep learning.
Subjects: G600 Software Engineering
G700 Artificial Intelligence
W300 Music
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology > Digital Media Technology
Depositing User: Jason Hockman
Date Deposited: 12 Jul 2019 07:43
Last Modified: 12 Jul 2019 07:43
URI: http://www.open-access.bcu.ac.uk/id/eprint/6180

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