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

Abstract

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.

Application of Machine Learning for the Spatial Analysis of Binaural Room Impulse Responses

“…The process was then run again to detect the location of the reflected component, and each segment was checked to ensure only audio pertaining to the reflected component was present (see Figure 5 for an example BRIR with window locations). When dealing with BRIRs measured in less controlled environments, a method for systematically detecting discrete reflections in the BRIR is required, and various methods have been proposed in the literature to detect reflections in impulse responses, including [4,[33][34][35].…”

Acoustic Reflector Localization and Classification

“…From the estimated n e,l , the distance between the reflection image sources and the mi-crophone array are obtained as ρ e,l = n e,l c 0 . By also having θ e,l and φ e,l , the image sources are localized in spherical coordinates, as it was done in our image source direction and ranging (ISDAR) [16]. The loudspeaker image bisection (LIB) [27] is then used to calculate M e,l , that is the midpoint between the image source position, in Cartesian coordinates, B e,l and the loudspeaker B 0,l : M e,l = (B e,l + B 0,l )/2.…”

“…Midpoints that are related to reflections classified as large M e,l,C e,l =1 are converted to planes by employing our ISDAR-LIB method (for a detailed description, please, refer to [16]). Looking towards the six spatial directions, the closest planes to the microphone array are selected to generate the room shoebox model.…”

“…A 2D geometric approach was presented in [15]. Its full 3D extension was later proposed in [16], where reflectors were localized by constructing ellipsoids, having axes proportional to the reflection TOAs. Despite the good performance, it had a high computational cost.…”

Object-Based Reverberation for Spatial Audio

“…In particular, the peak detection, frequency filter estimation, and mixing time estimation were improved. Peak detection used the Clustered Dynamic Programming Projected Phase-Slope Algorithm (C-DYPSA) [53] to extract the six strongest peaks (ranked by ampltiude) detected across all 48 RIR channels. Each detected peak was segmented with a window of L = 64 samples, and then a delay and sum beamformer (DSB) was steered in 3D to estimate the DOA.…”