WaivOps POP-ROK: Open Audio Resources for Machine Learning in Music

POP-ROK Dataset

POP-ROK is an open audio dataset featuring an uncurated collection of synthetic drum recordings in the style of pop rock music. It includes 5,378 audio loops recorded in uncompressed stereo WAV format, along with paired JSON files intended for the supervised training of generative AI audio models.

Overview

The POP-ROK Dataset was developed by sonifying a collection of approximately 30 acoustic drum kits with a paired MIDI dataset covering basic rhythm patterns, excluding toms. Data augmentation included a random drum-swapping method to generate unique drum kits and reverb simulations to represent various room sizes. This dataset is intended for training or fine-tuning AI models in rhythm notation with paired drum note labels, aiming to replicate the sound of live drumming.

The primary purpose of this dataset is to provide accessible content for machine learning applications in music and audio. Potential use cases include generative music, feature extraction, tempo detection, audio classification, rhythm analysis, drum synthesis, music information retrieval (MIR), sound design and signal processing.

Specifications

5,378 audio loops (approximately 24 hours)

16-bit WAV format

Tempo range: 100-130 BPM

Paired label data (WAV + JSON)

Variational drum patterns

Subgenre styles (Pop, classic rock, soft rock, country)

A JSON file is provided for referencing and converting MIDI note numbers to text labels. You can update the text labels to suit your preferences.

License

This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance.

The POP-ROK dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Additional Info

For audio examples or more information about this dataset, please refer to the GitHub repository.