用音乐制作插件生成混响数据，提升人声去混响效果
ReverbFX: A Dataset of Room Impulse Responses Derived from Reverb Effect Plugins for Singing Voice Dereverberation
- 基于音乐制作插件生成多样混响脉冲响应
- 插件生成数据训练模型在人工混响场景更优
- 适合语音处理与音频增强方向研究者
我们提出 ReverbFX，一个专为歌声去混响研究设计的房间脉冲响应（RIR）数据集。与以往基于真实录音的 RIR 数据集不同，ReverbFX 的 RIR 来自音乐制作中常用的混响效果插件。我们利用该数据集开展全面实验，评估受人工混响影响的歌声去混响挑战。使用 ReverbFX 训练了两个最先进的生成模型，结果表明：在人工混响场景下，基于插件生成 RIR 训练的模型性能优于基于真实 RIR 训练的模型。
原文摘要 · Abstract (English)
We present ReverbFX, a new room impulse response (RIR) dataset designed for singing voice dereverberation research. Unlike existing datasets based on real recorded RIRs, ReverbFX features a diverse collection of RIRs captured from various reverb audio effect plugins commonly used in music production. We conduct comprehensive experiments using the proposed dataset to benchmark the challenge of dereverberation of singing voice recordings affected by artificial reverbs. We train two state-of-the-art generative models using ReverbFX and demonstrate that models trained with plugin-derived RIRs outperform those trained on realistic RIRs in artificial reverb scenarios.
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