用扩散模型无监督分离麦克风阵列的混音语音，无需知道阵列结构。
ArrayDPS: Unsupervised Blind Speech Separation with a Diffusion Prior
- 基于扩散后验采样，通过优化逼近房间声学与麦克风间传递函数。
- 在无监督条件下达到接近有监督方法的语音分离效果（SDR）。
- 仅需单人语音扩散模型和混音数据，不依赖阵列几何信息。
盲语音分离（BSS）旨在从麦克风阵列录制的音频混合信号中分离出多个语音源。该问题具有挑战性，因为它是典型的盲逆问题：麦克风阵列几何结构、房间脉冲响应（RIR）以及语音源均未知。本文提出ArrayDPS，以无监督、阵列无关且生成式的方式解决此问题。其核心思想基于扩散后验采样（DPS），但不同于通常可计算似然的情况，ArrayDPS需通过独立优化问题近似似然。该优化解用于逼近房间声学特性及麦克风间的相对传输函数。这些近似结果与扩散先验共同迭代于ArrayDPS采样流程中，最终输出分离的语音源。整个方法仅需一个简单的单人语音扩散模型作为先验，并结合麦克风采集的混合信号；无需任何麦克风阵列信息。实验结果表明，ArrayDPS优于所有基线无监督方法，且在语音分离度（SDR）上与有监督方法相当。音频演示见：https://arraydps.github.io/ArrayDPSDemo/。
原文摘要 · Abstract (English)
Blind Speech Separation (BSS) aims to separate multiple speech sources from audio mixtures recorded by a microphone array. The problem is challenging because it is a blind inverse problem, i.e., the microphone array geometry, the room impulse response (RIR), and the speech sources, are all unknown. We propose ArrayDPS to solve the BSS problem in an unsupervised, array-agnostic, and generative manner. The core idea builds on diffusion posterior sampling (DPS), but unlike DPS where the likelihood is tractable, ArrayDPS must approximate the likelihood by formulating a separate optimization problem. The solution to the optimization approximates room acoustics and the relative transfer functions between microphones. These approximations, along with the diffusion priors, iterate through the ArrayDPS sampling process and ultimately yield separated voice sources. We only need a simple single-speaker speech diffusion model as a prior along with the mixtures recorded at the microphones; no microphone array information is necessary. Evaluation results show that ArrayDPS outperforms all baseline unsupervised methods while being comparable to supervised methods in terms of SDR. Audio demos are provided at: https://arraydps.github.io/ArrayDPSDemo/.
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