# A Comprehensive Review on Music Transcription
## 9 Future Directions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Dataset Name** & **Propose** & **Description** \\ \hline \multirow{4}{*}{TONAS [120]} & \multirow{4}{*}{Polyphonic vocal} & TONAS offers a unique collection of 72 Flamenco songs annotated in semitones, presenting a diverse range of vocal styles and melodic structures. It consists of three exppella singing styles, Deblas, and two variants of Martinete. \\ \hline \multirow{4}{*}{[PERSON] chorales [121]} & \multirow{4}{*}{Polyphonic vocal} & The [PERSON] chorales dataset, comprising harmonized vocal compositions, is widely used for studying both melodic and harmonic elements in polyphonic vocal music. \\ \hline \multirow{4}{*}{MAPS [23]} & \multirow{4}{*}{Single-instrument (Piano)} & The MIDI aligned piano sounds (MAPS) dataset centers on solo piano recordings, making it an excellent resource for piano transcription tasks. It is composed of isolated notes, random-pitch chords, and usual western chords. The corrected version of the MAPS dataset has been created by screening the error using an algorithmic strategy [122]. MAPS contains 40 GB of music, which represents about 65 h of recordings. \\ \hline \multirow{4}{*}{MAESTRO [66]} & \multirow{4}{*}{Single-instrument (Piano)} & MAESTRO offers a collection of classical piano performances, enriching the dataset’s diversity and enabling researchers to explore nuances in solo piano transcription techniques. The dataset is composed of about 200 h of paired audio and MIDI recordings. The audio and MIDI files align with an approximate 3 millisecond range. There are three versions of MAESTRO: V1.0,0, V2.0,0, and V3.0.0, which are of the sizes 87 GB, 103 GB, and 101 GB, respectively. \\ \hline \multirow{4}{*}{Slakh2100 [123]} & \multirow{4}{*}{Multi-instrument} & The Synthesized Lakh (Slakh) Dataset comprises multi-track audio and synchronized MIDI data intended for music source separation and the automated transcription of multiple instruments. Utilizing high-quality sample-based virtual instruments, individual MIDI tracks are generated from the Lakh MIDI Dataset v0.1. These MIDI tracks are then combined to create musical mixtures, resulting in 2100 tracks and corresponding aligned MIDI files in this release, known as Slakh2100. These tracks are synthesized from 187 patches categorized into 34 classes. \\ \hline \multirow{4}{*}{MusicNet [124]} & \multirow{4}{*}{Multi-instrument} & MusicNet is a publicly available dataset for music feature representation learning, containing 330 classical music recordings with aligned labels. These labels encompass 1,299,329 individual segments within 34 h of recordings and are based on 513 distinct instrument/note combinations. The dataset is skewed towards [PERSON]’s compositions and Solo Piano due to their popularity, but researchers can augment it to enhance the coverage of less-represented instruments like Flute and Oboe. \\ \hline \multirow{4}{*}{URMP [125]} & \multirow{4}{*}{Multi-instrument} & URMP, the University of Rochester Multimodal Music Performance dataset, offers recordings of 44 chamber music performances, serving as a valuable resource for understanding and transcribing interactions between multiple instruments in ensemble contexts. \\ \cline{1-1} \cline{2-2}  & & URMP, the University of Rochester Multimodal Music Performance dataset, offers recordings of 44 chamber music performances, serving as a valuable resource for understanding and transcribing interactions between multiple instruments in ensemble contexts. \\ \hline \end{tabular}
\end{table} 
Table 1: Cont. used in many different fields of artificial intelligence including automatic music transcription. Inspired by the successful sequence-to-sequence transfer learning in natural language processing, one of the recent works demonstrates the effectiveness of a general-purpose transformer model in transcribing various combinations of instruments across multiple datasets [31]. Additionally, another study takes the transformer model into account for the purpose of piano transcription.