Uncertainty Quantification and Risk Control for Multi-Speaker Sound Source Localization

TL;DR

This paper introduces two novel uncertainty quantification methods for multi-speaker sound source localization, providing reliable confidence measures in challenging environments, with theoretical guarantees and practical validation.

Contribution

It develops conformal prediction-based approaches for SSL that handle known and unknown source counts, offering finite-sample guarantees and improved reliability.

Findings

Methods achieve reliable coverage guarantees in simulations.

Performance remains robust across reverberation levels.

Approaches effectively estimate source count and localization regions.

Abstract

Reliable Sound Source Localization (SSL) plays an essential role in many downstream tasks, where informed decision making depends not only on accurate localization but also on the confidence in each estimate. This need for reliability becomes even more pronounced in challenging conditions, such as reverberant environments and multi-source scenarios. However, existing SSL methods typically provide only point estimates, offering limited or no Uncertainty Quantification (UQ). We leverage the Conformal Prediction (CP) framework and its extensions for controlling general risk functions to develop two complementary UQ approaches for SSL. The first assumes that the number of active sources is known and constructs prediction regions that cover the true source locations. The second addresses the more challenging setting where the source count is unknown, first reliably estimating the number of active sources and then forming corresponding prediction regions. We evaluate the proposed methods on extensive simulations and real-world recordings across varying reverberation levels and source configurations. Results demonstrate reliable finite-sample guarantees and consistent performance for both known and unknown source-count scenarios, highlighting the practical utility of the proposed frameworks for uncertainty-aware SSL.