Spatial active noise control (ANC) systems aim to control the noise field over a spatial region. However, when both the desired field and the interference field co-exist, typical ANC systems lack the ability to selectively reduce the interference field, resulting in distortions in the desired field. In this paper, we design a spherical-harmonic domain selective spatial ANC system to reduce the interference field and preserve the desired field. The designed system integrates a desired field extraction (DFE) process, a sound field reproduction (SER) process, and an ANC process. The DFE and SFR processes are implemented to first capture and then reproduce the desired field, while the ANC process reduces the pseudo residual field. The simulation study shows that compared to two existing ANC solutions, the proposed design achieves a better overall performance with an acceptable speech-distortion-ratio within the sweet area in a reverberant environment

Active Noise Control (ANC) over large regions of interest (ROI) traditionally requires numerous evenly distributed error microphones, which is often impractical and obstructive for human occupants. In this paper, we proposed a wave domain adaptive ANC algorithm using the joint information from multiple error spherical microphone arrays (SMAs) on the boundary of the ROI. By mapping the SMA recordings to the virtual sound field and introducing ℓ1norm on the virtual plane wave weights, the proposed method can achieve good noise reduction over the entire larger ROI, especially when the number of noise sources is finite and sparse. Our finding presents a significant advancement in spatial ANC, paving the way for more effective and unobtrusive solutions in a range of environments.

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.

Acoustic velocity vectors (AVVs) are related to the human's perception of sound at low frequencies and are widely used in Ambisonics. This letter proposes a spatial sound field reproduction algorithm called velocity matching, which reproduces the AVVs in the spherical listening region by matching the AVVs' spherical harmonic coefficients. Using the sound field translation formula, the spherical harmonic coefficients of the AVVs are derived from the spherical harmonic coefficients of the pressure, which can be measured by a higher-order microphone array. Unlike algorithms that only control the AVVs at discrete sweet spots, the proposed velocity matching algorithm manipulates the AVVs in the whole spherical listening region and allows the listener to move beyond the sweet spots. Simulations show the proposed velocity matching algorithm accurately reproduces the AVVs in the spherical listening region and requires fewer number of loudspeakers than pressure matching algorithm.

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.

This paper presents the image source method for simulating the observed signals in the time-domain on the boundary of a spherical listening region. A wideband approach is used where all derivations are in the time-domain. The source emits a sequence of spherical wave fronts whose amplitudes could be related to the far-field directional impulse responses of a loudspeaker. Geometric methods are extensively used to model the observed signals. The spherical harmonic coefficients of the observed signals are also derived.

This paper presents the time-domain wideband spherical microphone array impulse response generator (TDW-SMIR generator), which is a time-domain wideband image source method (ISM) for generating the room impulse responses captured by an open spherical microphone array. To incorporate loudspeaker directivity, the TDW-SMIR generator considers a source that emits a sequence of spherical wave fronts whose amplitudes are related to the loudspeaker directional impulse responses measured in the far-field. The TDW-SMIR generator uses geometric models to derive the time-domain signals recorded by the spherical microphone array. Comparisons are made with frequency-domain single band ISMs. Simulation results prove the results of the TDW-SMIR generator are similar to those of frequency-domain single band ISMs.

Spatial active noise control (ANC) aims to reduce unwanted acoustic noise over a continuous spatial region by generating an anti-noise field with secondary loudspeakers. Conventionally, spatial ANC is achieved by using complex error microphone arrays such as grid or spherical geometry, which are impractical and obstruct the users to enter the quiet region. Recently, the remote microphone technique has been introduced to spatial ANC systems without using error microphones inside the region of interest. However, this technique still requires an error microphone array during the tuning stage. In this paper, we further improve the remote microphone technique by introducing a spatial sound field recording method with a moving higher order microphone for the noise field recording (tuning stage) as well as secondary channel estimations (control stage). This eliminates the requirement for impractical microphone array geometries, typically required in existing spatial ANC solutions. The experimental data based simulation demonstrates the effectiveness of the proposed method on noise reduction over space with its feasible array design.

This paper presents a sound source localization method using an irregular microphone array embedded in a drone. Sound source localization is an integral function of drone audition systems which enables various applications of drones such as search and rescue missions. However, the audio recordings using the on-board microphones obscure the sound emitted by a source on the ground due to drone generated motor and propeller noise, thus leading to an extremely low signal-to-drone noise ratio (SdNR). In this paper, we propose a cross-correlation based direction of arrival (DOA) estimation technique using the time difference of arrival (TDOA) at different microphone pairs, with noise angular spectrum subtraction. Through the measured current-specific drone noise spectrum, noise suppression has been achieved from the multi-channel recordings. Experimental results show that the proposed method is capable of estimating the position in three-dimensional space for simultaneously active multiple sound sources on the ground at low SdNR conditions ($-30$dB), and localize two sound sources located at a certain azimuth angular separation with low prediction error comparable to the multiple signal classification (MUSIC) based algorithms and the generalized cross-correlation with phase transformation (GCC-PHAT) method. Due to its simplicity, applicability to any array geometry, and better robustness against drone noise, the proposed method increases the feasibility of localization under extreme SdNR levels.

All spatial active noise control (ANC) systems, when deployed in typical room environments, have time-varying acoustic channels between the secondary sources and the error microphones. The conventional online secondary path modeling techniques, which introduces additive auxiliary random noise to estimate the secondary paths, become challenging especially in a multichannel setup. In this work, we propose to use higher-order variable-directivity sound sources as secondary sources for spatial ANC, in which both the interior residual noise field within the control region and exterior sound field due to secondary source radiation are jointly controlled. The aim of controlling the exterior sound field is to minimize room reverberation generated by the secondary sources so that the secondary paths in the proposed algorithm can be approximated as free-field propagation and thus can be pre-calibrated. The system is implemented in an adaptive manner to track noise variations. The results show that the proposed method can effectively cancel spatial noise field and control exterior sound field at an acceptable low level in time-varying room environments.

Active noise control (ANC) over space is a well-researched topic where multi-microphone, multi-loudspeaker systems are designed to minimize the noise over a spatial region of interest. In this paper, we perform an initial study on the more complex problem of simultaneous noise control over multiple target regions using a single ANC system. In particular, we investigate the maximum active noise control performance over the multiple target regions, given a particular setup of secondary loudspeakers. The performance analysis is carried out using a wave-domain representation to best represent sound propagation over multiple regions in a given enclosure. Furthermore, given the global primary noise field and a fixed secondary source setup, a subspace method is exploited to evaluate the best system performance that could minimize the residual noise fields over multiple regions of interest. We provide experimental results to demonstrate the effectiveness of the proposed method.

The aim of spatial active noise control (ANC) is to attenuate noise over a certain space. Although a large-scale system is required to achieve spatial ANC, mode-domain signal processing makes it possible to reduce the computational cost and improve the performance. A higher-order source (HOS) has an advantage in sound field control due to its controllable directivity patterns. An array of HOS can suppress an undesired exterior sound propagation while occupying a smaller physical space than a conventional omnidirectional loudspeaker array. In this paper, we propose two types of adaptive algorithm for spatial ANC using HOSs, which provide a trade-off between efficiency and error robustness against loudspeaker placements. Numerical simulations in a reverberant environment show the efficacy of the proposed algorithms compared with the conventional multipoint adaptive spatial ANC algorithm.

Noise control and cancellation over a spatial region is a fundamental problem in acoustic signal processing. In this paper, we utilize wave-domain adaptive algorithms to iteratively calculate the secondary source driving signals and to cancel the primary noise field over the control region. We propose wave-domain active noise control algorithms based on two minimization problems: first, minimizing the wave-domain residual signal coefficients, and second, minimizing the acoustic potential energy over the region, and derive the update equations with respect to two variables, the loudspeaker weights and wave-domain secondary source coefficients. Simulation results demonstrate the effectiveness of the proposed algorithms, more specifically the convergence speed and the noise cancellation performance in terms of the noise reduction level and acoustic potential energy reduction level over the entire spatial region.

In this paper, we investigate active noise control over large 2D spatial regions when the noise source is sparsely distributed. The l1relaxation technique originated from compressive sensing is adopted and based on that we develop the algorithm for two cases: multipoint noise cancellation and wave domain noise cancellation. This results in two new variants (i) zero-attracting multi-point complex FxLMS and (ii) zero-attracting wave domain complex FxLMS. Both approaches use a feedback control system, where a microphone array is distributed over the boundary of the control region to measure the residual noise signals and a loudspeaker array is placed outside the microphone array to generate the anti-noise signals. Simulation results demonstrate the performance and advantages of the proposed methods in terms of convergence rate and spatial noise reduction levels.