用高斯过程与置信预测，精准定位声音源并给出可信范围。
Conformal Prediction for Manifold-based Source Localization with Gaussian Processes
- 结合高斯过程与流形学习，实现小样本下的声源定位。
- 在多种噪声环境下生成统计有效的预测区间，且范围更小。
- 适合对定位精度要求高的机器人听觉系统使用。
我们研究了在恶劣声学环境下声源定位的不确定性量化问题。由于噪声和混响等因素影响，定位结果存在显著不确定性，而现有方法仅提供点估计，缺乏不确定性描述。为解决此问题，本文采用置信预测（Conformal Prediction, CP）框架，该框架可在有限样本下提供统计有效的预测区间（PIs），且不依赖数据分布假设。然而，传统的归纳式置信预测（ICP）需要大量标注数据，难以满足定位场景需求。为此，本文提出一种基于流形的半监督定位方法，结合高斯过程回归（GPR）与高效的转导式置信预测（TCP）技术，专为GPR设计。实验表明，该方法在不同声学条件下均能生成统计有效的预测区间，且区间宽度显著小于基线方法。
原文摘要 · Abstract (English)
We address the problem of uncertainty quantification (UQ) in the localization of a sound source within adverse acoustic environments. Estimating the position of the source is influenced by various factors, such as noise and reverberation, leading to significant uncertainty. Quantifying this uncertainty is essential, particularly when localization outcomes impact critical decision-making processes, such as in robot audition, where the accuracy of location estimates directly influences subsequent actions. Despite this, common localization methods offer point estimates without quantifying the estimation uncertainty. To address this, we employ conformal prediction (CP)-a framework that delivers statistically valid prediction intervals (PIs) with finite-sample guarantees, independent of the data distribution. However, commonly used Inductive CP (ICP) methods require a large amount of labeled data, which can be difficult to obtain in the localization setting. To mitigate this limitation, we incorporate a semi-supervised manifold-based localization method using Gaussian process regression (GPR), with an efficient Transductive CP (TCP) technique, specifically designed for GPR. We demonstrate that our method generates statistically valid PIs across different acoustic conditions, while producing smaller intervals compared to baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。