Most recent advances in audio dereverberation focus almost exclusively on speech, leaving percussive and drum signals largely unexplored despite their importance in music production. Percussive dereverberation poses distinct challenges due to sharp transients and dense temporal structure. In this work, we propose a cold diffusion framework for dereverberating stereo drum stems (downmixes), modeling reverberation as a deterministic degradation process that progressively transforms anechoic signals into reverberant ones. We investigate two reverse-process parameterizations, Direct (next-state) and a Delta-normalized residual (velocity-style) prediction, and implement the framework using both a UNet and a diffusion Transformer backbone. The models are trained and evaluated on curated datasets comprising both acoustic and electronic drum recordings, with reverberation generated using a combination of synthetic and real room impulse responses. Extensive experiments on in-domain and fully out-of-domain test sets demonstrate that the proposed method consistently outperforms strong score-based and conditional diffusion baselines, evaluated using signal-based and perceptual metrics tailored to percussive audio.

翻译：最近音频去混响领域的进展几乎完全集中于语音信号，而打击乐和鼓点信号尽管在音乐制作中至关重要，却鲜有探索。打击乐去混响因其尖锐的瞬态和密集的时间结构而面临独特挑战。本文提出一种冷扩散框架，用于对立体声鼓组干声（缩混信号）进行去混响，将混响建模为一种确定性退化过程，逐步将无混响信号转化为混响信号。我们研究了两种逆过程参数化方法：直接（下一状态）预测和Delta归一化残差（速度风格）预测，并分别采用UNet和扩散Transformer主干网络实现该框架。模型在包含原声鼓和电子鼓录音的精选数据集上进行训练和评估，混响由合成房间脉冲响应和真实房间脉冲响应组合生成。针对域内和完全域外测试集的广泛实验表明，所提方法在基于信号和针对打击乐音频定制的感知指标上，始终优于强分数驱动基线和条件扩散基线。