用扩散模型盲解音频非线性失真，同时恢复信号与失真函数。
Estimation and Restoration of Unknown Nonlinear Distortion using Diffusion
- 用无条件训练的扩散模型，在推理时联合建模并反演非线性系统。
- 可成功还原硬/软削波、量化、整流、波折叠等失真，且立方Catmull-Rom样条拟合效果最佳。
- 适用于音乐、语音修复及模拟录音介质分析，性能媲美甚至优于有监督方法。
本文研究了未知非线性失真音频信号的恢复及所施加的无记忆非线性操作的识别问题，聚焦于非线性与原始输入信号均未知这一困难但实际重要的场景。提出的方法利用在吉他或语音信号上无条件训练的生成扩散模型，在推理时联合建模并反演非线性系统，输出无记忆非线性函数模型和恢复后的音频信号。成功案例包括硬/软削波、数字量化、半波整流和波折叠等非线性逆推。结果表明，测试中立方Catmull-Rom样条最适合作此类非线性的近似。对于吉他录音，与知情及监督方法相比，该盲方法在客观指标上至少相当；在语音失真恢复中，优于通用语音增强技术，有效还原原始音质。该方法可用于音频效果建模、音乐与语音录音修复及模拟录音介质表征。
原文摘要 · Abstract (English)
The restoration of nonlinearly distorted audio signals, alongside the identification of the applied memoryless nonlinear operation, is studied. The paper focuses on the difficult but practically important case in which both the nonlinearity and the original input signal are unknown. The proposed method uses a generative diffusion model trained unconditionally on guitar or speech signals to jointly model and invert the nonlinear system at inference time. Both the memoryless nonlinear function model and the restored audio signal are obtained as output. Successful example case studies are presented including inversion of hard and soft clipping, digital quantization, half-wave rectification, and wavefolding nonlinearities. Our results suggest that, out of the nonlinear functions tested here, the cubic Catmull-Rom spline is best suited to approximating these nonlinearities. In the case of guitar recordings, comparisons with informed and supervised methods show that the proposed blind method is at least as good as they are in terms of objective metrics. Experiments on distorted speech show that the proposed blind method outperforms general-purpose speech enhancement techniques and restores the original voice quality. The proposed method can be applied to audio effects modeling, restoration of music and speech recordings, and characterization of analog recording media.
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