# High-Quality and Reproducible Automatic Drum Transcription From Crowdsourced Data
## 3 Materials and Methods
### Training Procedure
#### 3.2.1 Datasets  
The first datasets considered were ENST, MDB, and RBMA, since they are publicly available and are often employed for ADT [25, 26, 27]. However, despite their wide use, these three datasets are very small and lack diversity in several aspects: ENST (we used the \"minus-one\" subset because it contains melodic instruments) contains recordings of a variety of musical genres, even though only three professionals on three different drum kits were involved in the performance; MDB also contains drum annotations for diverse musical genres, but only from 23 tracks of the Medley DB dataset [28]; and RBMA contains recordings of different artists from the 2013 Red Bull Music Academy ([[https://rbma.bandcamp.com/](https://rbma.bandcamp.com/)]([https://rbma.bandcamp.com/](https://rbma.bandcamp.com/)), accessed on 30 October 2023) but lacks diversity in musical genres as it focuses on electronic music. Consequently, these datasets do not effectively train complex models such as deep neural networks; this is especially true for drum instruments with very few onsets [2] (p. 4). Such a paucity of data is due to the difficulty and time cost of creating the annotations. As a possible solution, synthetic datasets have been generated from symbolic tracks found online. One of these datasets is TMIDT, created by [PERSON] et al. [2], which contains an amount of data two orders of magnitude larger than ENST, MDB, and RBMA and is more effective in training complex models. Even though training on TMIDT shows surprisingly good results, the synthesized audio lacks both the realism and the acoustic diversity of real-world tracks, and intuitively this hinders the performance of models trained on it. This intuition is supported by the improvements that result from adding real-world music tracks after training on synthetic data. Therefore, we only used TMIDT for training and not for testing. Specifically, the training on this dataset, as in [PERSON]'s work, was performed in two steps: first, the model was fitted exclusively on TMIDT; then, it was refined on the other non-synthetic datasets. An alternative to counter the aforementioned limitations of small or synthetic datasets is offered by crowdsourced annotations. This approach seeks to leverage the large number of annotations found on multiple sources online. [PERSON] et al. [4] followed this procedure to curate A2 MD, a dataset composed of MIDI and audio files scraped from the Internet. The \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & **Hours** & **Vocabulary** & **\#Tracks** & **Real Music** & **Beat** \\ \hline ENST [25] & 1.0 & 20 & 64 & ✓ & \\ MDB [26] & 0.4 & 21 & 23 & ✓ & \\ RBMA [27] & 1.7 & 24 & 30 & ✓ & ✓ \\ TMIDT [2] & 259 & 18 & 4197 & & ✓ \\ A2 MD [4] & 35 & 3 & 1565 & ✓ & ✓ \\ ADTOF-RGW [3] & 114 & 5 & 1739 & ✓ & ✓ \\ ADTOF-YT & 245 & 5 & 2924 & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of datasets for DTM. MIDI files came from the symbolic-only Lakh MIDI dataset [22], and the audio files were downloaded from platforms such as YouTube. Then, the annotations were synthesized to identify and align them with the original audio tracks. A total of 35 h of annotated music was curated this way and used to train a model whose performance was comparable to that of models trained with ENST, MDB, and RBMA. However, [PERSON]'s dataset was only used to train a model on three drum instruments, and the model was evaluated in a more challenging zero-shot manner compared to those trained with ENST, MDB, and RBMA. Because of these limitations, we did not make use of A2 MD. Instead, we used the dataset ADTOF [3], which for clarity we renamed here ADTOF-RGW (RGW is a reference to the Rhythm Gaming World website from which the tracks were downloaded). Compared to A2 MD, this dataset contains more data, has a larger five-class vocabulary, and was curated from audio and annotations files built for rhythm video games (e.g., Rock Band, Phaseshit, and Clone Hero).