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.Cadenza 新版本已发布，请确保使用最新版本。 请将CadenzaWoodwind引用为： 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：《Cadenza木管数据集：面向音乐信息检索与机器学习的合成四重奏》，载于《Data in Brief》（2024年），DOI：https://doi.org/10.1016/j.dib.2024.111199 本数据集为第二届Cadenza机器学习挑战赛（CAD2）的古典音乐重平衡任务提供训练与验证数据。Cadenza挑战赛旨在改善听障人群的音乐制作与处理体验。据世界卫生组织统计，全球共有4.3亿人患有致残性听力损失。助听器使用者在聆听音乐时常会遇到诸多问题，例如低音失真、难以感知全频段音乐（尤其是高频音），以及容易忽略乐曲中轻柔段落的表现力[1]。在一项预实验中，我们发现为听者提供滑块以调节古典音乐合奏中不同乐器的音量平衡，是一项广受认可的解决方案。 文件概况： CadenzaWoodwind：用于训练与验证的木管乐器小型合奏合成数据集。 EnsembleSet_Mix_1：用于训练与验证的合成数据集EnsembleSet[7]的子集（Mix_1渲染版本）。 调优用真实数据：Stereo_Reverb_Real_Data_For_Tuning.zip。 metadata.zip包含听力图、场景详情、目标增益值与压缩器设置。 压缩包内的音频文件均采用FLAC格式，JSON文件存储元数据。更多详情如下。