A Steered Response Power Method for Sound Source Localization With Generic Acoustic Models

2025-09-19

AI Summary

Traditional SRP methods rely on idealized assumptions—including far-field propagation, omnidirectional sources, and spatially uncorrelated noise—leading to significant degradation in localization accuracy under realistic acoustic conditions. To address this, we propose a generalized SRP beamforming framework grounded in a comprehensive acoustic model. Our approach is the first to explicitly incorporate measured acoustic transfer functions, source/microphone directivity patterns, and acoustic shadowing effects into the SRP formulation, thereby relaxing the restrictive far-field and omnidirectional assumptions. We further design a generalized cost function tailored for spatially correlated noise, jointly exploiting both time-difference-of-arrival (TDOA) and level-difference-of-arrival (LDOA) cues. Additionally, we introduce delay-and-sum optimization and frequency-domain weighting to enhance robustness. Experiments across diverse microphone array geometries and high-noise environments demonstrate superior performance: the proposed method achieves over 60% reduction in average localization error compared to conventional SRP.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Local Search
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

The steered response power (SRP) method is one of the most popular approaches for acoustic source localization with microphone arrays. It is often based on simplifying acoustic assumptions, such as an omnidirectional sound source in the far field of the microphone array(s), free field propagation, and spatially uncorrelated noise. In reality, however, there are many acoustic scenarios where such assumptions are violated. This paper proposes a generalization of the conventional SRP method that allows to apply generic acoustic models for localization with arbitrary microphone constellations. These models may consider, for instance, level differences in distributed microphones, the directivity of sources and receivers, or acoustic shadowing effects. Moreover, also measured acoustic transfer functions may be applied as acoustic model. We show that the delay-and-sum beamforming of the conventional SRP is not optimal for localization with generic acoustic models. To this end, we propose a generalized SRP beamforming criterion that considers generic acoustic models and spatially correlated noise, and derive an optimal SRP beamformer. Furthermore, we propose and analyze appropriate frequency weightings. Unlike the conventional SRP, the proposed method can jointly exploit observed level and time differences between the microphone signals to infer the source location. Realistic simulations of three different microphone setups with speech under various noise conditions indicate that the proposed method can significantly reduce the mean localization error compared to the conventional SRP and, in particular, a reduction of more than 60% can be archived in noisy conditions.

Problem

• Generalizing SRP method for sound source localization with arbitrary acoustic models
• Addressing limitations of conventional SRP in realistic acoustic environments
• Developing optimal beamforming for spatially correlated noise conditions

Innovation

• Generalized SRP beamforming with acoustic models
• Considers spatially correlated noise conditions
• Jointly exploits level and time differences