从音乐混音中直接提取鼓点样本，提升音色保真度。
DOSE : Drum One-Shot Extraction from Music Mixture
- 基于神经音频编解码语言模型，端到端提取鼓点。
- 引入起始瞬态损失，显著提升鼓点起始精度。
- 适用于电子音乐制作与音色设计，适合快速采样需求。
鼓点单次采样在音乐制作，尤其是音色设计和电子音乐中至关重要。本文提出鼓点单次提取任务，旨在从音乐混音中提取存在的鼓点单次样本。为此，我们构建了大规模随机排列的音乐混音与对应鼓点样本的数据集RMOD。提出的DOSE模型利用神经音频编解码语言模型实现端到端提取，跳过传统源分离步骤。此外，引入一种新型起始瞬态损失，以增强对鼓点初始瞬态的准确预测，这对保留音色特征至关重要。通过与基于源分离的方法对比，使用弗雷谢音频距离（FAD）和多尺度谱损失（MSS）评估，结果表明，加入起始损失的DOSE优于基线，能更准确、高质量地从音乐混音中提取鼓点。代码、模型检查点及音频示例可在https://github.com/HSUNEH/DOSE获取。
原文摘要 · Abstract (English)
Drum one-shot samples are crucial for music production, particularly in sound design and electronic music. This paper introduces Drum One-Shot Extraction, a task in which the goal is to extract drum one-shots that are present in the music mixture. To facilitate this, we propose the Random Mixture One-shot Dataset (RMOD), comprising large-scale, randomly arranged music mixtures paired with corresponding drum one-shot samples. Our proposed model, Drum One- Shot Extractor (DOSE), leverages neural audio codec language models for end-to-end extraction, bypassing traditional source separation steps. Additionally, we introduce a novel onset loss, designed to encourage accurate prediction of the initial transient of drum one-shots, which is essential for capturing timbral characteristics. We compare this approach against a source separation-based extraction method as a baseline. The results, evaluated using Frechet Audio Distance (FAD) and Multi-Scale Spectral loss (MSS), demonstrate that DOSE, enhanced with onset loss, outperforms the baseline, providing more accurate and higher-quality drum one-shots from music mixtures. The code, model checkpoint, and audio examples are available at https://github.com/HSUNEH/DOSE
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