High-Quality and Reproducible Automatic Drum Transcription from Crowdsourced Data

Author:

Zehren Mickaël1ORCID,Alunno Marco2ORCID,Bientinesi Paolo1ORCID

Affiliation:

1. Department of Computing Science, Umeå Universitet, 90187 Umeå, Sweden

2. Department of Music, Universidad EAFIT, Medellín 050022, Colombia

Abstract

Within the broad problem known as automatic music transcription, we considered the specific task of automatic drum transcription (ADT). This is a complex task that has recently shown significant advances thanks to deep learning (DL) techniques. Most notably, massive amounts of labeled data obtained from crowds of annotators have made it possible to implement large-scale supervised learning architectures for ADT. In this study, we explored the untapped potential of these new datasets by addressing three key points: First, we reviewed recent trends in DL architectures and focused on two techniques, self-attention mechanisms and tatum-synchronous convolutions. Then, to mitigate the noise and bias that are inherent in crowdsourced data, we extended the training data with additional annotations. Finally, to quantify the potential of the data, we compared many training scenarios by combining up to six different datasets, including zero-shot evaluations. Our findings revealed that crowdsourced datasets outperform previously utilized datasets, and regardless of the DL architecture employed, they are sufficient in size and quality to train accurate models. By fully exploiting this data source, our models produced high-quality drum transcriptions, achieving state-of-the-art results. Thanks to this accuracy, our work can be more successfully used by musicians (e.g., to learn new musical pieces by reading, or to convert their performances to MIDI) and researchers in music information retrieval (e.g., to retrieve information from the notes instead of audio, such as the rhythm or structure of a piece).

Publisher

MDPI AG

Subject

General Medicine

Reference40 articles.

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2. Vogl, R., Widmer, G., and Knees, P. (2018, January 4–8). Towards multi-instrument drum transcription. Proceedings of the 21th International Conference on Digital Audio Effects (DAFx-18), Aveiro, Portugal.

3. Zehren, M., Alunno, M., and Bientinesi, P. (2021, January 7–12). ADTOF: A large dataset of non-synthetic music for automatic drum transcription. Proceedings of the 22nd International Society for Music Information Retrieval Conference (ISMIR), Online.

4. Wei, I.C., Wu, C.W., and Su, L. (2021, January 6–11). Improving Automatic Drum Transcription Using Large-Scale Audio-to-Midi Aligned Data. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.

5. Ishizuka, R., Nishikimi, R., Nakamura, E., and Yoshii, K. (2020, January 7–10). Tatum-Level Drum Transcription Based on a Convolutional Recurrent Neural Network with Language Model-Based Regularized Training. Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Auckland, New Zealand.

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