用稀疏麦克风数据精准估计声场幅值分布，突破传统测量限制
Learning Magnitude Distribution of Sound Fields via Conditioned Autoencoder
- 基于条件自编码器，输入输出依源/接收位置与频率动态调整
- 仅需少量麦克风即可高精度还原声场幅值，数值模拟验证有效
- 适合声学成像、空间音频重建等场景，尤其适用于相位不可靠时
提出一种基于学习的方法，从空间稀疏的声场测量中估计声学传递函数（ATF）的幅值分布。当相位测量不可靠或无法获取时，该方法具有广泛应用价值，尤其在空间音频领域。所提方法采用神经网络实现ATF幅值估计，其核心在于输入输出层依据声源与接收位置及频率进行条件化，并引入潜在变量聚合模块，可视为声场基展开的自编码器扩展。数值仿真结果表明，本方法仅需少量接收器即可准确估计ATF幅值。
原文摘要 · Abstract (English)
A learning-based method for estimating the magnitude distribution of sound fields from spatially sparse measurements is proposed. Estimating the magnitude distribution of acoustic transfer function (ATF) is useful when phase measurements are unreliable or inaccessible and has a wide range of applications related to spatial audio. We propose a neural-network-based method for the ATF magnitude estimation. The key feature of our network architecture is the input and output layers conditioned on source and receiver positions and frequency and the aggregation module of latent variables, which can be interpreted as an autoencoder-based extension of the basis expansion of the sound field. Numerical simulation results indicated that the ATF magnitude is accurately estimated with a small number of receivers by our proposed method.
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