Spatial audio reproduction and translation for virtual, augmented, and extended reality applications require an efficient representation of the recorded sound fields. In this paper, we investigate the possible sparse representations of the sound field recorded by multiple microphones in reverberant environments. We combine the Complex Orthogonal Matching Pursuit (COMP) algorithm with the concept of distributed virtual sound sources to propose a sparse sound field representation. The technique uses recordings from a grid of microphones and transforms them into a sparse representation featuring a selected set of active virtual sources. Using simulation, we evaluate the proposed COMP approach with LASSO and IRLS methods in a reverberant room of regular size with a ceiling-mounted microphone array.

Conventional multiple-point active noise control (ANC) systems require placing error microphones within the region of interest (ROI), inconveniencing users. This paper designs a feasible monitoring microphone arrangement placed outside the ROI, providing a user with more freedom of movement. The soundfield within the ROI is interpolated from the microphone signals using a physics-informed neural network (PINN). PINN exploits the acoustic wave equation to assist soundfield interpolation under a limited number of monitoring microphones, and demonstrates better interpolation performance than the spherical harmonics method in simulations. An ANC system is designed to take advantage of the interpolated signal to reduce noise signal within the ROI. The PINN-assisted ANC system reduces noise more than that of the multiple-point ANC system in simulations.

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.

Large-scale Room Impulse Response (RIR) measurements are required to accurately determine a room’s acoustic response to different source-listener configurations. RIR reconstruction methods are often used to reduce these measurement costs. Prior knowledge of room acoustic parameters can ensure reliable and robust RIR reconstruction. This paper proposes a method to reconstruct RIRs based on reflection source locations and time-frequency-direction-dependent reflection magnitude response estimated from a single spherical microphone array measurement. These input parameters are learned by applying the eigenbeam spatial correlation method and von Mises–Fisher (vMF)-based directivity modeling. According to the performance evaluation, the composition of the learned features in the RIR reconstruction formulation successfully preserves the objective characteristics of the real room.