SDLGASJun 24, 2024

Maximum Likelihood Estimation of the Direction of Sound In A Reverberant Noisy Environment

arXiv:2406.17103v2
Originality Incremental advance
AI Analysis

This addresses the challenge of accurate sound localization for applications like audio processing or robotics, but appears incremental as it builds on existing principles with specific adaptations.

The paper tackles the problem of estimating sound direction in noisy, reverberant environments by developing a method based on SNR-adaptive features from acoustic wave decomposition, and demonstrates its effectiveness with measured data across various microphone array configurations.

We describe a new method for estimating the direction of sound in a reverberant environment from basic principles of sound propagation. The method utilizes SNR-adaptive features from time-delay and energy of the directional components after acoustic wave decomposition of the observed sound field to estimate the line-of-sight direction under noisy and reverberant conditions. The effectiveness of the approach is established with measured data of different microphone array configurations under various usage scenarios.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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