# High-Quality and Reproducible Automatic Drum Transcription From Crowdsourced Data
###### 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). automatic drum transcription; crowdsourced dataset; self-attention mechanism; tatum 2023  
2023/02/02/02/02/02/023/02/  
(e.g., a bass drum may be misinterpreted for a low tom or yield to the perception of an extra low tom on top of it). Due to this complexity, the most promising methods for DTM, as identified by the review work of [PERSON] et al. [1], use supervised deep learning (DL) algorithms--large models trained with labeled datasets. However, despite their good performance, there is still a large margin for improvement. In fact, it is acknowledged that these algorithms are limited by the amount of training data available; by contrast, due to the fact that their generation is labor-intensive, existing datasets are usually small. Furthermore, because data are often copyrighted, datasets are made publicly available only in very few cases. As a result, the available data do not cover the huge diversity that is found in music and is needed to train DL algorithms to reach their full potential. In response to this data paucity, most of the approaches either tackle a simplified variant of DTM or rely on techniques other than supervised learning. For example, the problem can be simplified by restricting the desired content of the transcription to a vocabulary that includes only the three most common drum instruments, i.e., the bass drum, snare drum, and hi-hat, or by using techniques such as transfer or unsupervised learning that can mitigate the lack of training data--indeed, one of the most competitive methods, proposed by [PERSON] et al., is based on transfer learning from a large amount of synthetic data [2]. Nevertheless, limiting the scope of the problem and mitigating the data paucity are not satisfying solutions. Instead, it would be preferable to increase the amount of training data, which has been shown to be possible with two recent large-scale datasets: ADTOF [3] and A2 MD [4]. Thanks to the large amount of crowdsourced, publicly available, non-synthetic data from the Internet, these datasets have improved the supervised training of DTM models. However, these datasets have not yet been well exploited for three reasons: no exploration of an optimal DL architecture has been conducted; due to crowdsourcing, the data contain discrepancies and are biased to specific music genres; only a few training procedures have been evaluated.