{"title":"Conformal Prediction for Manifold-based Source Localization with Gaussian Processes","authors":"Vadim Rozenfeld, Bracha Laufer Goldshtein","doi":"arxiv-2409.11804","DOIUrl":null,"url":null,"abstract":"We tackle the challenge of uncertainty quantification in the localization of\na sound source within adverse acoustic environments. Estimating the position of\nthe source is influenced by various factors such as noise and reverberation,\nleading to significant uncertainty. Quantifying this uncertainty is essential,\nparticularly when localization outcomes impact critical decision-making\nprocesses, such as in robot audition, where the accuracy of location estimates\ndirectly influences subsequent actions. Despite this, many localization methods\ntypically offer point estimates without quantifying the estimation uncertainty.\nTo address this, we employ conformal prediction (CP)-a framework that delivers\nstatistically valid prediction intervals with finite-sample guarantees,\nindependent of the data distribution. However, commonly used Inductive CP (ICP)\nmethods require a substantial amount of labeled data, which can be difficult to\nobtain in the localization setting. To mitigate this limitation, we incorporate\na manifold-based localization method using Gaussian process regression (GPR),\nwith an efficient Transductive CP (TCP) technique specifically designed for\nGPR. We demonstrate that our method generates statistically valid uncertainty\nintervals across different acoustic conditions.","PeriodicalId":501284,"journal":{"name":"arXiv - EE - Audio and Speech Processing","volume":"26 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Audio and Speech Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.11804","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We tackle the challenge of uncertainty quantification 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, many localization methods
typically offer point estimates without quantifying the estimation uncertainty.
To address this, we employ conformal prediction (CP)-a framework that delivers
statistically valid prediction intervals with finite-sample guarantees,
independent of the data distribution. However, commonly used Inductive CP (ICP)
methods require a substantial amount of labeled data, which can be difficult to
obtain in the localization setting. To mitigate this limitation, we incorporate
a 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 uncertainty
intervals across different acoustic conditions.