Amy Bastine

Comprehensive room acoustic characterization requires resolving reflection behavior across time, frequency, and space. The recently proposed eigenbeam–vMF-based analyzer provides a framework for this by modeling the reflection field as a time–frequency-dependent directional power distribution, estimated via spatial correlation of eigenbeams (ambisonics) and parameterized using von Mises–Fisher clustering. This formulation enables a unified and interpretable description of anisotropic early reflections, their transition into diffuse reverberation, and frequency-dependent acoustic behavior. Prior work showed that the analyzer reliably captures these features using higher-order ambisonics from a 32-channel spherical microphone array (SMA) and that constraining the same array to the first order still led to retaining the dominant features. This paper investigates whether this capability extends to first-order microphone arrays with sparser spatial sampling for more economical and practical deployment. A comparative study is conducted in a recording studio with variable wall panels (wood and felt), evaluating a four-channel first-order array against a 32-channel SMA. The results reveal distinct acoustic differences between panel settings, which are consistent across both arrays. While the SMA captures finer spatial detail and prolonged anisotropic reflections more effectively, the first-order array demonstrates potential for preliminary room acoustic assessments by identifying room mode frequencies, dominant reflection directions, and highly reflective surfaces.

Sound source localization in reverberant environments remains a challenging problem, particularly when precise position estimation is required. Existing DOA estimation methods, while effective in determining sound direction, often fall short of accurate position estimation in adverse acoustic conditions due to their inherent constraints. In this paper, we propose Multi-Dictionary Orthogonal Matching Pursuit (MD-OMP), extending sparse recovery methods by jointly exploiting spatial, temporal, and spectral information for source position estimation. MD-OMP iteratively selects candidate grid entries that maximize joint correlation across time frames and frequency bins, thereby enhancing localization accuracy under reverberant conditions. We also introduce a generalized extension of the algorithm to accommodate an arbitrary number of multidimensional domains, thereby supporting advanced applications such as higher-order source modeling and scenarios with dominant frequency components. To validate the effectiveness of MD-OMP, we arrange microphones along the room walls to form a perimeter array and conduct both extensive simulations and a practical experiment in a conference room setting. Numerical simulations under various acoustic conditions-including different reverberation times, grid densities, frequency ranges, room dimensions, and source types-show that MD-OMP consistently outperforms group-sparsity-based and TDOA-based benchmark methods in localization accuracy. Experimental results in a realistic office environment further verify its practical applicability. Overall, MD-OMP achieves accurate and robust source position estimation across diverse and challenging scenarios.

This paper introduces RSoANU, a dataset of real multichannel room impulse responses (RIRs) obtained in a recording studio. Compared to the current publicly available datasets, RSoANU distinguishes itself by featuring RIRs captured using both a 32-channel spherical microphone array (mh acoustics em32 Eigenmike) and a B-format soundfield microphone array (Rode NT-SF1). The studio incorporates variable wall panels in felt and wood options, with measurements conducted for two configurations: all panels set to wood or felt. Three source positions that emulate typical performance locations were considered. RIRs were collected over a planar receiver grid spanning the room, with the microphone array centered at a height of 1.7 m. The paper includes an analysis of acoustic parameters derived from the dataset, revealing notable distinctions between felt and wood panel environments. Felt panels exhibit faster decay, higher clarity, and superior definition in mid-to-high frequencies. The analysis across the receiver grid emphasizes the impact of room geometry and source–receiver positions on reverberation time and clarity. The study also notes spatial variations in parameters obtained from the two microphone arrays, suggesting potential for future research into their specific capabilities for room acoustic characterization.

Acoustic imaging is powerful in collecting spatial information of acoustic sources into a visual representation. In this paper, we focus on the analysis of the exterior acoustic field captured by a circular array of microphones. With a proper parametrization based on angles, we map the directions of arrival of sources as a function of the microphone locations, thus obtaining an acoustic image called “angular space”. Therefore, we introduce a linear transform to enable analysis and synthesis operations for mapping the microphone pressures onto the angular space using local space-time Fourier analysis. We prove the ability of this representation to combine global information coming from multiple arrays in a single acoustic image that can be processed and manipulated. Examples of source localization applications in simulated and measured scenarios show the effectiveness of the proposed method obtaining results comparable with state-of-the-art methods.

Head-related transfer functions (HRTFs) are crucial for spatial soundfield reproduction in virtual reality applications. However, obtaining personalized, high-resolution HRTFs is a time-consuming and costly task. Recently, deep learning-based methods showed promise in interpolating high-resolution HRTFs from sparse measurements. Some of these methods treat HRTF interpolation as an image super-resolution task, which neglects spatial acoustic features. This paper proposes a spherical convolutional neural network method for HRTF interpolation. The proposed method realizes the convolution process by decomposing and reconstructing HRTF through the Spherical Harmonics (SHs). The SHs, an orthogonal function set defined on a sphere, allow the convolution layers to effectively capture the spatial features of HRTFs, which are sampled on a sphere. Simulation results demonstrate the effectiveness of the proposed method in achieving accurate interpolation from sparse measurements, outperforming the SH method and learning-based methods.

The knowledge of frequency-dependent spatiotemporal features of the reflected soundfield is essential in optimizing the perception quality of spatial audio applications. For this purpose, we need a reliable room acoustic analyzer that can conceive the spatial variations in a decaying reflected soundfield according to the frequency-dependent surface properties and source directivity. This paper introduces a time-frequency-dependent angular reflection power distribution model represented by a von Mises–Fisher (vMF) mixture function to facilitate manifold analysis of a reverberant soundfield. The proposed approach utilizes the spatial correlation of higher-order eigenbeams to deduce the directional reflection power vectors, which are then synthesized into a vMF mixture model. The experimental study demonstrates the directional power variations of early reflections and late reverberations across different frequencies. This work also introduces a measure called the directivity time-span to quantify the duration of anisotropic reflections before it decays into a totally diffused field. We validate the subband performance by comparing it with the eigenbeam multiple signal classification method. The results prove the influence of source position, source directivity, and room environment in the distribution of reflection power, whereas the directivity time-span behaves independent of the source positions.

Modal decays and modal power distribution in acoustic environments are key factors in deciding the perceptual quality and performance accuracy of audio applications. This paper presents the application of the eigenbeam spatial correlation method in estimating the time-frequency-dependent directional reflection powers and modal decay times. The experimental results evaluate the application of the proposed technique for two rooms with distinct environments using their room impulse response (RIR) measurements recorded by a spherical microphone array. The paper discusses the classical concepts behind room mode distribution and the reasons behind their complex behavior in real environments. The time-frequency spectrum of room reflections, the dominant reflection locations, and the directional decay rates emulate a realistic response with respect to the theoretical expectations. The experimental observations prove that our model is a promising tool in characterizing early and late reflections, which will be beneficial in controlling the perceptual factors of room acoustics.