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Steered Response Power for Sound Source Localization: a tutorial review.

Eric Grinstein¹, Elisa Tengan², Bilgesu Çakmak²

¹Department of Electrical and Electronic Engineering, Imperial College London, London, UK.

EURASIP Journal on Audio, Speech, and Music Processing

|November 15, 2024

Summary

This summary is machine-generated.

The Steered Response Power (SRP) method is a popular technique for sound source localization (SSL). This work reviews SRP variants and introduces X-SRP, a flexible algorithm for improved sound source localization.

Keywords:

Acoustic signal processing Beamforming DOA estimation Sound Source Localization Steered Response Power

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Area of Science:

Signal Processing
Acoustics
Computational Audition

Background:

The Steered Response Power (SRP) method has been a cornerstone of Sound Source Localization (SSL) for over 30 years.
Its effectiveness in moderately reverberant and noisy conditions has led to numerous extensions.
These extensions aim to enhance computational efficiency, enable multi-source localization, and improve performance in challenging acoustic environments.

Purpose of the Study:

To provide a comprehensive review of the Steered Response Power (SRP) method and its variants, with a specific focus on SRP-PHAT.
To introduce eXtensible-SRP (X-SRP), a novel, generalized, and modularized framework for SRP algorithms.
To facilitate the implementation of existing and future SRP extensions within a unified structure.

Main Methods:

Systematic literature review of over 200 papers on SRP and its variants.
Development of the eXtensible-SRP (X-SRP) algorithm, a generalized and modular SRP framework.
Implementation of X-SRP in Python, incorporating selected extensions from the reviewed literature.

Main Results:

A comprehensive overview of the evolution and diversification of the SRP method for Sound Source Localization.
The introduction of X-SRP, a flexible and extensible platform for SRP-based SSL.
A functional Python implementation of X-SRP with selected literature extensions, enabling practical application and further research.

Conclusions:

The SRP method remains a vital tool for Sound Source Localization, with ongoing research addressing its limitations.
X-SRP offers a standardized and adaptable approach to implementing and evaluating SRP variants.
The provided Python implementation serves as a valuable resource for researchers in acoustic signal processing and sound source localization.