The MAESTRO dataset contains over 200 hours of paired audio and MIDI recordings from ten years of International Piano-e-Competition. The MIDI data includes key strike velocities and sustain/sostenuto/una corda pedal positions. Audio and MIDI files are aligned with ∼3 ms accuracy and sliced to individual musical pieces, which are annotated with composer, title, and year of performance. Uncompressed audio is of CD quality or higher (44.1–48 kHz 16-bit PCM stereo). Source: https://magenta.tensorflow.org/datasets/maestro Image Source: https://www.researchgate.net/figure/Results-generated-with-the-MAESTRO-dataset_fig3_333392458
MusicNet is a collection of 330 freely-licensed classical music recordings, together with over 1 million annotated labels indicating the precise time of each note in every recording, the instrument that plays each note, and the note's position in the metrical structure of the composition. The labels are acquired from musical scores aligned to recordings by dynamic time warping. The labels are verified by trained musicians; we estimate a labeling error rate of 4%. We offer the MusicNet labels to the machine learning and music communities as a resource for training models and a common benchmark for comparing results. Source: MusicNet
The Synthesized Lakh (Slakh) Dataset is a dataset for audio source separation that is synthesized from the Lakh MIDI Dataset v0.1 using professional-grade sample-based virtual instruments. This first release of Slakh, called Slakh2100, contains 2100 automatically mixed tracks and accompanying MIDI files synthesized using a professional-grade sampling engine. The tracks in Slakh2100 are split into training (1500 tracks), validation (375 tracks), and test (225 tracks) subsets, totaling 145 hours of mixtures. Source: http://www.slakh.com/ Image Source: http://www.slakh.com/ Audio Source: http://www.slakh.com/
ASAP is a dataset of 222 digital musical scores aligned with 1068 performances (more than 92 hours) of Western classical piano music. The scores are provided as paired MusicXML files and quantized MIDI files, and the performances as paired MIDI files and partially as audio recordings. Scores and performances are aligned with downbeat, beat, time signature, and key signature annotations. Source: ASAP: a dataset of aligned scores and performances for piano transcription
The CocoChorales Dataset CocoChorales is a dataset consisting of over 1400 hours of audio mixtures containing four-part chorales performed by 13 instruments, all synthesized with realistic-sounding generative models. CocoChorales contains mixes, sources, and MIDI data, as well as annotations for note expression (e.g., per-note volume and vibrato) and synthesis parameters (e.g., multi-f0). ## Dataset We created CocoChorales using two generative models produced by Magenta: Coconet and MIDI-DDSP. The dataset was created in two stages. First, we used a trained Coconet model to generate a large set of four-part chorales in the style of J.S. Bach. The output of this first stage is a set of note sequences, stored as MIDI, to which we assign a tempo and add random timing variations to each note (for added realism). In the second stage, we use MIDI-DDSP to synthesize these MIDI files into audio, resulting in audio clips that sound like the chorales were performed by live musicians. This MIDI-DDSP model was trained on URMP. We define a set of ensembles that consist of the following instruments, in Soprano, Alto, Tenor, Bass (SATB) order: <ul> <li><strong>String Ensemble</strong>: Violin 1, Violin 2, Viola, Cello.</li> <li><strong>Brass Ensemble</strong>: Trumpet, French Horn, Trombone, Tuba.</li> <li><strong>Woodwind Ensemble</strong>: Flute, Oboe, Clarinet, Bassoon.</li> <li><strong>Random Ensemble</strong>: Each SATB part is randomly assigned an instrument according to the following: <ul class="nested"> <li><em>Soprano</em>: Violin, Flute, Trumpet, Clarinet, Oboe.</li> <li><em>Alto</em>: Violin, Viola, Flute, Clarinet, Oboe, Saxophone, Trumpet, French Horn.</li> <li><em>Tenor</em>: Viola, Cello, Clarinet, Saxophone, Trombone, French Horn.</li> <li><em>Bass</em>: Cello, Double Bass, Bassoon, Tuba.</li> </ul> </li> </ul> Each instrument in the ensemble is synthesized separately, with annotations for the high-level expressions used for each note (e.g., vibrato, note volume, note brightness, etc; all expressions shown here, and more details in Sections 3.2 and B.3 of the MIDI-DDSP paper) as well as detailed low-level annotations for synthesis parameters (e.g., f<sub>0</sub>’s, amplitudes of each harmonic, etc). Because the MIDI-DDSP model skews sharp, we randomly applied pitch augmentation to the f<sub>0</sub>’s (see Figure 2, here) to . All four audio clips for each instrument in the ensemble are then mixed together to produce an example in the dataset. Because all of the data in CocoChorales originate from generative models, all of the annotations perfectly correspond to the audio data. All in all, the dataset contains 240,000 examples, 60,000 mixes from each one of the four ensemble types above. Each ensemble has its own train/validation/test split All of the audio is 16 kHz, 16-bit PCM data. Each example contains: <ul> <li>A mixture</li> <li>Source audio for all four instruments <ul class="nested"> <li>Gain applied to each source</li> </ul> </li> <li>MIDI with tempo and precise timing</li> <li>The name of the ensemble with instrument names</li> <li>Note expression annotations for every note: <ul class="nested"> <li>Volume, Volume Fluctuation, Volume Peak Position, Vibrato, Brightness, and Attack Noise used by MIDI-DDSP to synthesize every note (see Sections 3.2 and B.3 of the MIDI-DDSP paper for more details)</li> </ul> </li> <li>Synthesis parameters for every source (250 Hz): <ul class="nested"> <li>Fundamental frequency (f<sub>0</sub>), amplitude, amplitude of all harmonics, filtered noise parameters</li> <li>Amount of pitch augmentation applied</li> </ul> </li> </ul> ## Further Details A detailed view of the contents of the CocoChorales dataset is provided at this link. # Download For download instructions, please see this github page. The compressed version of the full dataset is 2.9 Tb, and the uncompressed version is larger than 4 Tb. There is a "tiny" version for download as well. MD5 Hashes for all zipped files in the download are provided here. # License The CocoChorales dataset was made by Yusong Wu and is available under the Creative Commons Attribution 4.0 International (CC-BY 4.0). # How to Cite If you use CocoChorales in your work, we ask that you cite the following paper where it was introduced: Yusong Wu, Josh Gardner, Ethan Manilow, Ian Simon, Curtis Hawthorne, and Jesse Engel. “The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling.” arXiv preprint, arXiv:2209.14458, 2022. You can also use the following bibtex entry: @article{wu2022chamber, title = {The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling}, author = {Wu, Yusong and Gardner, Josh and Manilow, Ethan and Simon, Ian and Hawthorne, Curtis and Engel, Jesse}, journal={arXiv preprint arXiv:2209.14458}, year = {2022}, }
MAPS – standing for MIDI Aligned Piano Sounds – is a database of MIDI-annotated piano recordings. MAPS has been designed in order to be released in the music information retrieval research community, especially for the development and the evaluation of algorithms for single-pitch or multipitch estimation and automatic transcription of music. It is composed by isolated notes, random-pitch chords, usual musical chords and pieces of music. The database provides a large amount of sounds obtained in various recording conditions.
ENST-Drums: an extensive audio-visual database for drum signals processing Olivier Gillet and Gaël Richard GET / ENST, CNRS LTCI, 37 rue Dareau, 75014 Paris, France The ENST-Drums database is a large and varied research database for automatic drum transcription and processing: Three professional drummers specialized in different music genres were recorded. Total duration of audio material recorded per drummer is around 75 minutes. Each drummer played his own drum kit. Each sequence used either sticks, rods, brushes or mallets to increase the diversity of drum sounds. The drum kits themselves are varied, ranging from a small, portable, kit with two toms and 2 cymbals, suitable for jazz and latin music ; to a larger rock drum set with 4 toms and 5 cymbals. Each sequence is recorded on 8 individual audio channels, is filmed from two angles, and is fully annotated
A MIDI dataset of 500 4-part chorales generated by the KS_Chorus algorithm, annotated with results from hundreds of listening test participants, with 500 further unannotated chorales.
This dataset contains transcriptions of the electric guitar performance of 240 tablatures, rendered with different tones. The goal is to contribute to automatic music transcription (AMT) of guitar music, a technically challenging task. Activity signals were captured by attaching a special hexaphonic pickup to each string of an electric guitar and using a JUCE program to control a digital audio workstation (DAW) to automatically re-render the audio recordings of the “Direct Input” (DI) using different amplifiers (Amps), including low-gain amps and high-gain ones. A new collecting pipeline was employed to reduce the effort of manual inspection. The final dataset contains six copies of 118 minutes of guitar playing, each copy being associated with a different timbre. The new dataset, named “EGDB,” is constructed in this way to account for the diverse timbre associated with electric guitar. Having multiple guitar tones makes it possible to test a trained model on held-out unseen tones for generalizability. <span style="color:grey; opacity: 0.6">( Image Source: Frame Harirak )</span>
Guitar-TECHS is a comprehensive dataset featuring a variety of guitar techniques, musical excerpts, chords, and scales. These elements are performed by diverse musicians across various recording settings. Guitar-TECHS incorporates recordings from two stereo microphones: an egocentric microphone positioned on the performer’s head and an exocentric microphone placed in front of the performer. It also includes direct input recordings and microphoned amplifier outputs, offering a wide spectrum of audio inputs and recording qualities. All signals and MIDI labels are properly synchronized. Its multi-perspective and multi-modal content makes Guitar-TECHS a valuable resource for advancing data-driven guitar research, and to develop robust guitar listening algorithms.
MIR-ST500 Good for the following task: Singing transcription (singing pitch to music note conversion) Used in several papers published by Roger Jang's Lab
We redistribute a suite of datasets as part of the YourMT3 project. The license for redistribution is attached. ## YourMT3 Dataset Includes: - Slakh - MusicNet (original and EM) - MAPS (not used for training) - Maestro - GuitarSet - ENST-drums - EGMD - MIR-ST500 Restricted Access - CMedia Restricted Access - RWC-Pop (Bass and Full) Restricted Access - URMP - IDMT-SMT-Bass
This publicly available data is synthesised audio for woodwind quartets including renderings of each instrument in isolation. The data was created to be used as training data within Cadenza's second open machine learning challenge (CAD2) for the task on rebalancing classical music ensembles. The dataset is also intended for developing other music information retrieval (MIR) algorithms using machine learning. It was created because of the lack of large-scale datasets of classical woodwind music with separate audio for each instrument and permissive license for reuse. Music scores were selected from the OpenScore String Quartet corpus. These were rendered for two woodwind ensembles of (i) flute, oboe, clarinet and bassoon; and (ii) flute, oboe, alto saxophone and bassoon. This was done by a professional music producer using industry-standard software. Virtual instruments were used to create the audio for each instrument using software that interpreted expression markings in the score. Convolution reverberation was used to simulate a performance space and the ensembles mixed. The dataset consists of the audio and associated metadata Nineteen scores were randomly selected from the OpenScore String Quartet corpus. The synthesis of these as woodwind ensembles was performed by a sound engineering professional using professional software. The scores were loaded into the music notation software Steinberg's Dorico. The string parts were allocated to flute, oboe, clarinet (or alto saxophone) and bassoon. Virtual instruments were used to create the audio for each instrument: Miroslav Philharmonik 2 by IK Multimedia for the saxophone and Intimate Studio Winds by 8Dio for the other parts. The quartets were then imported into Avid's Pro Tools where they were mixed and reverberation added. Metadata were generated..