Cadenza Challenge (CAD2): databases for rebalancing classical music task

Cadenza

A new version has been published.

Please ensure you are working with the last version.

Please, cite CadenzaWoodwind as

Gerardo Roa Dabike , Trevor J. Cox , Alex J. Miller , Bruno M. Fazenda , Simone Graetzer , Rebecca R. Vos , Michael A. Akeroyd , Jennifer Firth , William M. Whitmer , Scott Bannister , Alinka Greasley , Jon P. Barker , The Cadenza Woodwind Dataset: Synthesised Quartets for Music Information Retrieval and Machine Learning, Data in Brief (2024), doi: https://doi.org/10.1016/j.dib.2024.111199

This is the training and validation data for the rebalancing classic music task from the Second Cadenza Machine Learning Challenge (CAD2).

The Cadenza Challenges are improving music production and processing for people with a hearing loss. According to The World Health Organization, 430 million people worldwide have a disabling hearing loss. Hearing aid users report several issues when listening to music, including distortion in the bass, difficulties in perceiving the full range of the music, especially high-frequency pitches, and a tendency to miss the impact of quieter parts of compositions [1]. In a pilot study, we found giving listeners sliders to allow them to rebalance different instruments in a classical music ensemble was desirable.

Overview of files:

CadenzaWoodwind. Synthesized dataset of small ensembles of woodwind instruments for training and validation.

EnsembleSet_Mix_1. A subset of the synthesised EnsembleSet [7] for training and validation (Mix_1 render).

Real Data for Tuning: Stereo_Reverb_Real_Data_For_Tuning.zip.

metadata.zip contains audiograms, scene details, target gains and compressor settings.

The audio files are in FLAC format in the .zip archives. The json files contain metadata.

More details below.