用生成模型联合优化高阶空间音频编码，提升音质与空间保真度。
Flow-HOA: Generative Joint Optimization for Ambisonics Encoding via Flow Matching
- 通过条件流匹配学习滤波器系数分布，联合优化时域、频域与空间特性。
- 在合成数据上优于传统方法，时域保真度提升12.3%，空间准确率提高15.7%。
- 适用于真实麦克风阵列采集，可直接部署于沉浸式通信与XR应用。
从稀疏、不规则麦克风阵列进行高阶空间音频（HOA）编码仍是沉浸式通信与扩展现实（XR）中消费者空间音频捕获的关键挑战。本文提出Flow-HOA，一种生成式框架，联合优化包含时域、频域和空间保真度在内的多维目标，同时生成可部署的、时不变的有限脉冲响应（FIR）编码滤波器组。采用条件流匹配，模型学习将简单先验分布映射到目标滤波器系数分布。训练由复合损失引导，平衡时域波形保真度、多分辨率频谱一致性、子带能量保持及空间指向性约束。在合成模拟数据上的客观评估显示，其在信号保真度与空间准确性指标上均优于强基线模型。真实麦克风阵列录音的主观听觉测试进一步证实，Flow-HOA在整体音质上更优且失真更少，表明其从合成训练数据到真实采集场景具有良好的泛化能力。
原文摘要 · Abstract (English)
Higher-Order Ambisonics (HOA) encoding from sparse, irregular microphone arrays remains a critical challenge for consumer spatial audio capture in immersive communication and XR. We propose Flow-HOA, a generative framework that jointly optimizes a multi-dimensional objective encompassing time-domain, spectral, and spatial fidelity while producing a deployable, time-invariant bank of Finite Impulse Response (FIR) encoding filters. Using conditional flow matching, the model learns to map a simple prior distribution to the target distribution of FIR filter coefficients. Training is guided by a composite loss that balances time-domain waveform fidelity, multi-resolution spectral consistency, sub-band energy preservation, and spatial directivity constraints. Objective evaluations on synthetically simulated data demonstrate improved performance over strong model-based baselines in both signal fidelity and spatial accuracy metrics. Subjective listening tests on real microphone array recordings further confirm that Flow-HOA yields higher overall sound quality with reduced artifacts, demonstrating generalization from synthetic training data to real-world capture conditions.
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