Hockman, Jason

Lerch, Alexander

Southall, Carl

Wu, Chih-Wei

English

BCU Open Access

MDB DRUMS: AN ANNOTATED SUBSET OF MEDLEYDB FORAUTOMATIC DRUM TRANSCRIPTIONCarl Southall1, Chih-Wei Wu2, Alexander Lerch2, Jason Hockman11 DMT Lab, Birmingham City University2 Center for Music Technology, Georgia Institute of Technology{carl.southall, jason.hockman}@bcu.ac.uk, {cwu307, alexander.lerch}@gatech.eduABSTRACTIn this paper we present MDB Drums, a new dataset forautomatic drum transcription (ADT) tasks. This datasetis built on top of the MusicDelta subset of the MedleyDBdataset, taking advantage of real-world recordings in multi-track format. The dataset is comprised of a variety of gen-res, providing a balanced pool for developing and evalu-ating ADT models with respect to various musical styles.To reduce the cost of the labor-intensive process of man-ual annotation, a semi-automatic process was utilised inboth the annotation and quality control processes. The pre-sented dataset consists of 23 tracks with a total of 7994onsets. These onsets are divided into 6 classes based ondrum instruments or 21 subclasses based on playing tech-niques. Every track consists of a drum-only track as wellas multiple accompanied tracks, enabling audio files con-taining different combinations of instruments to be used inthe ADT evaluation process.1. INTRODUCTIONAutomatic drum transcription (ADT) is a task that con-cerns the extraction and detection of drum events from astream of audio signals. Like many research fields that relyon data-driven approaches, the advance of ADT researchis linked to the quality of the existing annotated datasets.To date, there are a few publicly available datasets such asENST drums 1 and IDMT-SMT drums 2 , which are com-monly used in recent ADT related studies [3, 4]. How-ever, as discussed in [4], most of the existing datasets onlycontain annotations of basic techniques (e.g., strikes, crosssticks, rim shots), and more detailed annotations on tech-niques such as flam, drag, and buzz rolls are missing. Ad-ditionally, the existing datasets are limited in different as-1 http://perso.telecom-paristech.fr/∼grichard/ENST-drums, last ac-cess: 02/10/172 https://www.idmt.fraunhofer.de/en/business units/m2d/smt/drums.html,last access: 02/10/17c© Carl Southall, Chih-Wei Wu, Alexander Lerch, JasonHockman. Licensed under a Creative Commons Attribution 4.0 Interna-tional License (CC BY 4.0). Attribution: Carl Southall, Chih-Wei Wu,Alexander Lerch, Jason Hockman. “MDB Drums: An annotated sub-set of MedleyDB for Automatic Drum Transcription”, Extended abstractsfor the Late-Breaking Demo Session of the 18th International Society forMusic Information Retrieval Conference, Suzhou, China, 2017.Figure 1. Overview of the creation process of MDBDrumsdataset.pects such as variety of musical styles and complexity ofdrum patterns. In this paper we present MDB Drums, anewly annotated drum dataset to address these limitations.2. DATASET CREATION2.1 OverviewThe creation process of MDB Drums is shown in Fig.1.Since the objective of this project was to create a realisticdrum dataset without introducing the extra cost of record-ing and post-processing, we chose to annotate an existingdataset. The MedleyDB dataset [1] was chosen as it con-tains multi-track recordings of the drums, allowing for eas-ier annotation. As the original purpose of the dataset wasnot for ADT tasks, the recording quality of drum tracksvaries widely across the dataset, which introduced a layerof inconsistency. As a result, the MusicDelta subset wasselected as it contains recordings of better quality with rea-sonable durations. More details are presented in the fol-lowing sections.2.2 Annotation StageDataset creation began with the annotation stage, whichconsisted of two steps: 1) onset detection and 2) manualannotation. To facilitate the process, we used the Onset-Detector algorithm from the madmom [2] library on eachof the audio files. The use of the drum-only tracks in Med-leyDB allowed the onset detector to consistently achievereliable results. Next, the extracted onset times were im-ported into Sonic Visualizer 3 for refinement by human an-3 http://www.sonicvisualiser.org, last access: 02/10/17Table 1. Onset classes in MDB Drums dataset.Class Subclass Description OnsetsKD KD kick drum 1539SDSD snare drum 1510SDB snare drum: brush 332SDD snare drum: drag 2SDF snare drum: flam 11SDG snare drum: ghost note 790SDNS snare drum: no snare 9HHCHH hi-hat: closed 1847OHH hi-hat: open 269PHH hi-hat: pedal 523TTHIT high tom 4MHT high-mid tom 26HFT high floor tom 14LFT low floor tom 46CYRDC ride cymbal 835RDB ride cymbal: bell 16CRC crash cymbal 126CHC china cymbal 15SPC splash cymbal 10OTSST side stick 38TMB tambourine 32notators. Each onset was annotated with its correspondingclass, and missing onsets were added.2.3 Examination StageTo ensure the consistency and accuracy of the resulting an-notations, all files were assessed automatically and verifiedmanually in the examination stage. For the automatic ex-amination, three checks were implemented: a check forinvalid class labels (i.e., labels with incorrect names), acheck for duplicate labels within a 50ms window, and acheck for three or more different labels occurring withinthe 50ms window. These rules were determined heuristi-cally based on the annotators’ domain knowledge. For themanual examination, a cross-check was conducted on allof the tracks. This required the two annotators to exam-ine each other’s assignments. Additionally, the dataset wasexamined by an external reviewer who was not involved inthe annotation stage.3. DATASET DETAILSThe MDB Drums dataset consists of a total of 23 trackswith an average length of 54 seconds. However, as thedataset contains multi-track files (i.e., all the isolated in-strumental tracks are included), a variety of combinationscan be easily generated (e.g., drum + guitar, drum + bassguitar). In total there are 7994 onsets divided across 6classes, namely the kick drum (KD), snare drum (SD), hi-hat (HH), toms (TT), cymbals (CY), and other percussion(OT). Table 1 highlights additional categorisation of theonsets into the 21 subclasses based on playing technique.The full dataset is available for download from the MDBDrums Github repository. 44 https://github.com/CarlSouthall/MDBDrums, last access: 02/10/173.1 MIREX SplitPrior to public release, the MDBDrums dataset was used inthe MIREX 2017 drum transcription evaluation. Both thedrum-only and full-mix tracks were used for the evaluationresulting in 46 tracks in total. For comparison against theachieved results, the training and tests splits used can alsobe found in the Github repository.4. CONCLUSIONThe contributions of the presented dataset can be summa-rized as follows: first, this dataset covers a diverse rangeof genres (e.g., such as Rock, Country, Disco, Reggae, andJazz) which provides a more representative sample pool ofthe real-world music than the existing datasets. Second,all tracks are real-world recordings, which include drumsplayed by musicians. As these are real drum tracks—asopposed to synthesized or sample-based audio—they arecapable of capturing the musical expression of percussion-ists and reflect the difficulty of ADT in real-world record-ings. Third, this dataset includes the most commonly useddrum classes (e.g., kick drum, snare drum and hi-hat) aswell as additional drum classes (e.g., toms and cymbals)in conjunction with detailed categorisation for each class(e.g., flam, drag, and roll for snare drum), encouraging fur-ther ADT studies such as the detection of playing tech-nique. Finally, all tracks contain multi-track files (e.g.,drum-only tracks and multiple accompanied tracks) allow-ing for a more diverse range of instrument combinations tobe used in the evaluation of ADT systems.5. ACKNOWLEDGEMENTSThe authors would like to thank Rachel Bittner and theMedleyDB team for the creation of the MedleyDB datasetand also Maciek Tomczak for helping to review the anno-tations.6. REFERENCES[1] Rachel M Bittner, Justin Salamon, Mike Tierney,Matthias Mauch, Chris Cannam, and Juan PabloBello. MedleyDB: A multitrack dataset for annotation-intensive MIR research. In Proc. Intl. Society for MusicInformation Retrieval Conf. (ISMIR), 2014.[2] Sebastian Bo¨ck, Filip Korzeniowski, Jan Schlu¨ter, Flo-rian Krebs, and Gerhard Widmer. madmom: A newPython audio and music signal processing library. InProc. of the ACM Intl. Conf. on Multimedia, Amster-dam, The Netherlands, 2016.[3] Carl Southall, Ryan Stables, and Jason Hockman. Au-tomatic drum transcription using bi-directional recur-rent neural networks. In Proc. Intl. Society for MusicInformation Retrieval Conf. (ISMIR), 2016.[4] Chih-Wei Wu and Alexander Lerch. On drum playingtechnique detection in polyphonic mixtures. In Proc.Intl. Society for Music Information Retrieval Conf. (IS-MIR), 2016.

MDB Drums: An annotated subset of MedleyDB for automatic drum transcription

Birmingham City University Open Access Repository

http://www.open-access.bcu.ac.uk/6179/1/Southall2017a.pdf

MDB Drums: An annotated subset of MedleyDB for automatic drum transcription

Abstract

Similar works

Full text

Available Versions

BCU Open Access

Birmingham City University Open Access Repository