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/
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
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..