GiantMIDI-Piano: A Large-Scale MIDI Dataset for Classical Piano Music

Qiuqiang Kong, Bochen Li, Jitong Chen, Yuxuan Wang
2022 Transactions of the International Society for Music Information Retrieval  
Symbolic music datasets are important for music information retrieval and musical analysis. However, there is a lack of large-scale symbolic datasets for classical piano music. In this article, we describe the creation of the GiantMIDI-Piano (GP) dataset containing 38,700,838 transcribed notes and 10,855 unique solo piano works composed by 2,786 composers. We extract the names of music works and the names of composers from the International Music Score Library Project (IMSLP). We search and
more » ... load their corresponding audio recordings from the Internet. We further create a curated subset containing 7,236 works composed by 1,787 composers where the titles of downloaded audio recordings contain the surnames of composers. We apply a convolutional neural network to detect solo piano works. Then, we transcribe those solo piano recordings into Musical Instrument Digital Interface (MIDI) files using a high-resolution piano transcription system. Each transcribed MIDI file contains the onset, offset, pitch, and velocity attributes of piano notes and pedals. GiantMIDI-Piano includes 90% live performance MIDI files and 10% sequence input MIDI files. We analyse the statistics of GiantMIDI-Piano and show pitch class, interval, trichord, and tetrachord frequencies of six composers from different eras to show that GiantMIDI-Piano can be used for musical analysis. We evaluate the quality of GiantMIDI-Piano in terms of solo piano detection F1 scores, metadata accuracy, and transcription error rates. We release the source code for acquiring the GiantMIDI-Piano dataset at https://
doi:10.5334/tismir.80 fatcat:yecuq5dw3fbqvknxc5gncc6cyy