Spatial sampling and beamforming for spherical microphone arrays

Spherical microphone arrays have been recently studied for spatial sound recording, speech communication, and sound field analysis for room acoustics and noise control. Complementary theoretical studies presented progress in spatial sampling and beamforming methods. This paper reviews recent results in spatial sampling that facilitate a wide range of spherical array configurations, from a single rigid sphere to free positioning of microphones. The paper then presents an overview of beamforming methods recently presented for spherical arrays, from the widely used delay-and-sum and Dolph-Chebyshev, to the more advanced optimal methods, typically performed in the spherical harmonics domain

3D sound field analysis using circular higher-order microphone array

This paper proposes the theory and design of circular higher-order microphone arrays for 3D sound field analysis using spherical harmonics. Through employing the spherical harmonic translation theorem, the local spatial sound fields recorded by each higher-order microphone placed in the circular arrays are combined to form the sound field information of a large global spherical region. The proposed design reduces the number of the required sampling points and the geometrical complexity of microphone arrays. We develop a two-step method to calculate sound field coefficients using the proposed array structure, i) analytically combine local sound field coefficients on each circular array and ii) solve for global sound field coefficients using data from the first step. Simulation and experimental results show that the proposed array is capable of acquiring the full 3D sound field information over a relatively large spherical region with decent accuracy and computational simplicity.This work was supported under the Australian Research Councils Discovery Projects funding scheme (project no. DP140103412)

Sound field decomposition based on two-stage neural networks

A method for sound field decomposition based on neural networks is proposed. The method comprises two stages: a sound field separation stage and a single-source localization stage. In the first stage, the sound pressure at microphones synthesized by multiple sources is separated into one excited by each sound source. In the second stage, the source location is obtained as a regression from the sound pressure at microphones consisting of a single sound source. The estimated location is not affected by discretization because the second stage is designed as a regression rather than a classification. Datasets are generated by simulation using Green's function, and the neural network is trained for each frequency. Numerical experiments reveal that, compared with conventional methods, the proposed method can achieve higher source-localization accuracy and higher sound-field-reconstruction accuracy.Comment: 31 pages, 16 figure

Active Noise Control Over Space: A Wave Domain Approach

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.DP14010341

Direction of Arrival Estimation in the Spherical Harmonic Domain using Subspace Pseudo-Intensity Vectors

Direction of Arrival (DOA) estimation is a fundamental problem in acoustic signal processing. It is used in a diverse range of applications, including spatial filtering, speech dereverberation, source separation and diarization. Intensity vector-based DOA estimation is attractive, especially for spherical sensor arrays, because it is computationally efficient. Two such methods are presented which operate on a spherical harmonic decomposition of a sound field observed using a spherical microphone array. The first uses Pseudo-Intensity Vectors (PIVs) and works well in acoustic environments where only one sound source is active at any time. The second uses Subspace Pseudo-Intensity Vectors (SSPIVs) and is targeted at environments where multiple simultaneous sources and significant levels of reverberation make the problem more challenging. Analytical models are used to quantify the effects of an interfering source, diffuse noise and sensor noise on PIVs and SSPIVs. The accuracy of DOA estimation using PIVs and SSPIVs is compared against the state-of-the-art in simulations including realistic reverberation and noise for single and multiple, stationary and moving sources. Finally, robust performance of the proposed methods is demonstrated using speech recordings in real acoustic environments

Augmented Intensity Vectors for Direction of Arrival Estimation in the Spherical Harmonic Domain

Pseudointensity vectors (PIVs) provide a means of direction of arrival (DOA) estimation for spherical microphone arrays using only the zeroth and the first-order spherical harmonics. An augmented intensity vector (AIV) is proposed which improves the accuracy of PIVs by exploiting higher order spherical harmonics. We compared DOA estimation using our proposed AIVs against PIVs, steered response power (SRP) and subspace methods where the number of sources, their angular separation, the reverberation time of the room and the sensor noise level are varied. The results show that the proposed approach outperforms the baseline methods and performs at least as accurately as the state-of-the-art method with strong robustness to reverberation, sensor noise, and number of sources. In the single and multiple source scenarios tested, which include realistic levels of reverberation and noise, the proposed method had average error of 1.5∘ and 2∘, respectively

Multiple source localization using spherical microphone arrays

Direction-of-Arrival (DOA) estimation is a fundamental task in acoustic signal processing and is used in source separation, localization, tracking, environment mapping, speech enhancement and dereverberation. In applications such as hearing aids, robot audition, teleconferencing and meeting diarization, the presence of multiple simultaneously active sources often occurs. Therefore DOA estimation which is robust to Multi-Source (MS) scenarios is of particular importance. In the past decade, interest in Spherical Microphone Arrays (SMAs) has been rapidly grown due to its ability to analyse the sound field with equal resolution in all directions. Such symmetry makes SMAs suitable for applications in robot audition where potential variety of heights and positions of the talkers are expected. Acoustic signal processing for SMAs is often formulated in the Spherical Harmonic Domain (SHD) which describes the sound field in a form that is independent of the geometry of the SMA. DOA estimation methods for the real-world scenarios address one or more performance degrading factors such as noise, reverberation, multi-source activity or tackled problems such as source counting or reducing computational complexity. This thesis addresses various problems in MS DOA estimation for speech sources each of which focuses on one or more performance degrading factor(s). Firstly a narrowband DOA estimator is proposed utilizing high order spatial information in two computationally efficient ways. Secondly, an autonomous source counting technique is proposed which uses density-based clustering in an evolutionary framework. Thirdly, a confidence metric for validity of Single Source (SS) assumption in a Time-Frequency (TF) bin is proposed. It is based on MS assumption in a short time interval where the number and the TF bin of active sources are adaptively estimated. Finally two analytical narrowband MS DOA estimators are proposed based on MS assumption in a TF bin. The proposed methods are evaluated using simulations and real recordings. Each proposed technique outperforms comparative baseline methods and performs at least as accurately as the state-of-the-art.Open Acces

Theory and Design of Feasible Active Noise Control Systems for 3D Regions

This thesis advances Active Noise Control (ANC) over three-dimensional (3D) space using feasible loudspeaker and microphone array systems. By definition, ANC reduces unwanted acoustic noise by generating an anti-noise signal(s) from secondary loudspeakers. The concept of spatial ANC aims to reduce unwanted acoustic noise over a continuous 3D region, by utilizing multiple microphones and multiple secondary loudspeakers to create a large-sized quiet zone for listeners in three-dimensional space. However, existing spatial ANC techniques are usually impractical and difficult to implement due to their strict hardware requirements and high computation complexity. Therefore, this thesis explores various aspects of spatial ANC, seeking algorithms and techniques to promote the reliability and feasibility of ANC over space in real-life applications. The spherical harmonic analysis technique is introduced as the basis of conventional spatial ANC systems. This technique provides an accurate representation of a given spatial sound field using higher-order microphone (spherical microphone array) recordings. Hence, the residual noise field in a spatial ANC system can be effectively captured spatially by applying the spherical harmonic technique. Incorporating conventional spatial ANC methods, we developed a series of algorithms and methods that optimize conventional methods regarding array geometries and ANC algorithms, towards improving the feasibility of a conventional spatial ANC system involving the spherical harmonic analysis. Overall, motivated by feasible and realistic designs for spatial ANC systems, work included in this thesis mainly solves the three problems of: (i) the impracticality of realizing spherical microphone and loudspeaker arrays, (ii) achieving secondary channel estimation with microphones remote from their desired locations, and (iii) unreasonable delays inherent to frequency domain spatial ANC methods. Based on our work, we have stepped towards achieving a spatial ANC system in a real-world environment for people to enjoy silence in the control region with the reliable usage of resources and algorithms. Several contributions of this work are: (i) designing a 3D spatial ANC system using multiple circular microphone and loudspeaker arrays instead of spherical arrays, (ii) proposing a 3D spatial ANC method with remote microphone technique such that noise reduction over a region is achieved with microphones remote from the region, (iii) proposing a secondary channel estimation method using a moving higher-order microphone such that usage of an error microphone array is not necessary, (iv) deriving a time domain spherical harmonic analysis method for open spherical microphone array recording with less delay than in the frequency domain, and (v) designing a feed-forward adaptive spatial ANC algorithm incorporating the time domain spherical harmonic analysis technique to better minimize the noise in the region of interest