Room geometry inference based on spherical microphone array eigenbeam processing

Abstract

The knowledge of parameters characterizing an acoustic environment, such as the geometric information about a room, can be used to enhance the performance of several audio applications. In this paper, a novel method for three-dimensional room geometry inference based on robust and high-resolution beamforming techniques for spherical microphone arrays is presented. Unlike other approaches that are based on the measurement and processing of multiple room impulse responses, here, microphone array signal processing techniques for uncontrolled broadband acoustic signals are applied. First, the directions of arrival (DOAs) and time differences of arrival (TDOAs) of the direct signal and room reflections are estimated using high-resolution robust broadband beamforming techniques and cross-correlation analysis. In this context, the main challenges include the low reflected-signal to background-noise power ratio, the low energy of reflected signals relative to the direct signal, and their strong correlation with the direct signal and among each other. Second, the DOA and TDOA information is combined to infer the room geometry using geometric relations. The high accuracy of the proposed room geometry inference technique is confirmed by experimental evaluations based on both simulated and measured data for moderately reverberant rooms.

Theory and design of compact hybrid microphone arrays on two-dimensional planes for three-dimensional soundfield analysis

“…Following the design procedure given in Sec. III, the radii of the rings are set to be 0.46, 0.4, 0.34, 0.28, 0.22, 0.16, and 0.1 m. Thus the number of microphone pairs on each ring are 21,19,17,13,11,9, and 7, respectively. To evaluate the performance of the proposed array system, we place a single point source of frequency 150-1150 Hz at ðR; h; /Þ ¼ (1.6 m, 60 , 90 ).…”

“…6,7 The existing spherical arrays can be categorized into two major categories: the open sphere model and the rigid sphere model. Both models are widely used in research applications, such as room geometry inference 13 and near field acoustic holography (NAH). 14 An inherent drawback of the open sphere model is the numerical illconditioning problem, which is due to the nulls in spherical Bessel functions, thus the diameter of the microphone array has to be chosen carefully.…”

Acoustic Reflector Localization and Classification

“…Nevertheless, a large space was required to be sampled, to correctly capture the sound wave. Spherical harmonics were employed in [10,11], to localize reflections. As in [12], they employed continuous signals, however, having issues with low SNRs.…”

Direction of arrival estimation of multiple acoustic sources using a maximum likelihood method in the spherical harmonic domain

“…The rotationally symmetric spatial directivity makes the spherical microphone array an appealing structure in many audio applications, among which the acoustic source localization, or the direction of arrival (DOA) estimation, plays an important role in speech enhancement [1], room impulse response analysis [2], and room geometry inference [3].…”

“…Various DOA estimation methods have been proposed, which can be generally classified as beamformer-based [2][3][4][5] and subspace-based [6][7]. The beamformer-based methods, such as those based on plane-wave decomposition (PWD) [4] and the minimum variance distortionless response (MVDR) beamformer [3], have the benefit of straightforward implementation, but suffer from low spatial resolution. The subspace-based methods, such as the multiple signal classification (MUSIC) [6], provide a high spatial resolution; however, they suffer from severe performance degradation when the signal-to-noise ratio (SNR) is low [8].…”