# High-Quality and Reproducible Automatic Drum Transcription From Crowdsourced Data
## 5 Conclusions  
Given an input audio recording, we considered the task of automatically transcribing drums in the presence of melodic instruments. In this study, we introduced a new large crowdsourced dataset and addressed three issues related to the use of datasets of this type: the lack of investigations of deep learning architectures trained on them, the discrepancies and bias in crowdsourced annotations, and their limited evaluation. Specifically, we first investigated four deep learning architectures that are well suited to leveraging large datasets. Starting from the well-known convolutional recurrent neural network (CRNN) architecture, we extended it to incorporate one or both of two mechanisms: itatum max-pooling and self-attention. We then mitigated discrepancies and bias in the training data by creating a new non-synthetic dataset (ADTOF-YT), which was significantly larger than other currently available datasets. ADTOF-YT was curated by gathering high-quality crowdsourced annotations, applying a cleansing procedure to make them compatible with the ADT task, and mixing it with other datasets to further diversify the training data. Finally, we evaluated many training procedures to quantify the contribution of each dataset to the performance of the model, in both zero-shot and non-zero-shot settings. The results can be summarized as follows: (1) No noticeable difference was observed among the four architectures we tested. (2) The combination of the newly introduced dataset ADTOF-YT with the existing ADTOF-RGW proved to train models better than any previously used datasets, synthetic or not. (3) We quantified the generalization capabilities of the model when tested on unseen datasets and concluded that good generalization was achieved only when large amounts of crowdsourced data were included in the training procedure. Thanks to this evaluation, we explored how to leverage crowdsourced datasets with different DL architectures and training scenarios. By achieving better results than the current state-of-the-art models, we improved the ability to retrieve information from music, be it for musicians who desire to learn a new musical piece, or for researchers who need to retrieve information from the location of the notes. Further, the models were approaching the Bayes error rate (irreducible error). In order to quantify the improvements that one can expect from a perfect classifier, in future work we aim to estimate this theoretical limit. Conceptualization, M.Z. and P.B. ; methodology, M.Z. ; software, M.Z. ; validation, M.Z. ; formal analysis, M.Z. ; investigation, M.Z. and P.B. ; resources, M.Z. and P.B. ; data curation, M.Z. ; writing--original draft preparation, M.Z. ; writing--review and editing, M.Z., M.A. and P.B. ; visualization, M.Z. ; supervision, P.B. and M.A. ; project administration, P.B. ; funding acquisition, P.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data available on request due to restrictions. The data presented in this study are available on request at [[https://github.com/MZehren/ADTOF](https://github.com/MZehren/ADTOF)]([https://github.com/MZehren/ADTOF](https://github.com/MZehren/ADTOF)) (accessed on 30 October 2023). The data are not publicly available due to the reasons described in Appendix B. The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) and the Swedish National Infrastructure for Computing (SNIC) at Chalmers Center for Computational Science and Engineering (C3 SE), partially funded by the Swedish Research Council through grant agreements no. 2022-06725 and no. 2018-05973. The authors declare no conflict of interest.