Acoustic Reflector Localization: Novel Image Source Reversion and Direct Localization Methods

TL;DR

This paper introduces new methods for localizing acoustic reflectors using room impulse responses, including an onset detector, image-source reversion, and a direct localization approach, with systematic experiments demonstrating their effectiveness.

Contribution

It presents five novel contributions, including an onset detector, image-source reversion with single and multiple loudspeakers, and a direct localization method called ETSAC.

Findings

ETSAC achieves the lowest localization errors.

ISDAR-LIB is 200 times faster than ETSAC.

Proposed methods outperform state-of-the-art in experiments.

Abstract

Acoustic reflector localization is an important issue in audio signal processing, with direct applications in spatial audio, scene reconstruction, and source separation. Several methods have recently been proposed to estimate the 3D positions of acoustic reflectors given room impulse responses (RIRs). In this article, we categorize these methods as "image-source reversion", which localizes the image source before finding the reflector position, and "direct localization", which localizes the reflector without intermediate steps. We present five new contributions. First, an onset detector, called the clustered dynamic programming projected phase-slope algorithm, is proposed to automatically extract the time of arrival for early reflections within the RIRs of a compact microphone array. Second, we propose an image-source reversion method that uses the RIRs from a single loudspeaker. It is constructed by combining an image source locator (the image source direction and range (ISDAR) algorithm), and a reflector locator (using the loudspeaker-image bisection (LIB) algorithm). Third, two variants of it, exploiting multiple loudspeakers, are proposed. Fourth, we present a direct localization method, the ellipsoid tangent sample consensus (ETSAC), exploiting ellipsoid properties to localize the reflector. Finally, systematic experiments on simulated and measured RIRs are presented, comparing the proposed methods with the state-of-the-art. ETSAC generates errors lower than the alternative methods compared through our datasets. Nevertheless, the ISDAR-LIB combination performs well and has a run time 200 times faster than ETSAC.