In this paper, the audio sound field generated by a focusing parametric array loudspeaker (PAL) is investigated both numerically and experimentally. A computationally efficient method is developed to calculate the quasilinear solution of both the Westervelt and Kuznetsov equations. The accuracy of generated sound field using Westervelt equation is initially examined, and the results are then compared with those obtained using the Kuznetsov equation. It is found that the focusing on the true focal location deteriorates the prediction accuracy using the Westervelt equation due to the strong and complicated local effects. To validate the numerical results against experimental data, a 37-channel focusing PAL prototype is constructed by using a field programmable gate array, metal-oxide semiconductor field-effect transistor drivers and the pulse train signal technique. Both the simulation and experimental results show that the audio sound pressure around the true focal location is increased, and the increment becomes larger at low audio frequencies and when the focal point moves close to the radiation surface, which is attributed to the strong local effects in the near field. It has been shown that a focusing PAL can improve the poor low frequency response for a conventional PAL without focusing, and the generated audio sound decays rapidly with the distance in the far field when compared to that without focusing.

Remote error sensing is required in active noise control systems when they are used to create a quiet zone in a noisy environment with the constraint that the error microphones cannot be inside the zone. The challenge in remote error sensing is to estimate the sound pressure in the target zone with a small number of physical microphones outside it. The spatial harmonic decomposition method uses wave domain sound field parameterisation to reduce the required number of the error microphones but can only provide accurate estimation below a certain frequency. This paper presents an improved approach to increase the effective frequency range based on the acoustic modelling. The simulation results demonstrate the proposed method can provide more than 20 dB noise reduction up to 1650 Hz for a quiet zone with a radius of 0.1 m by using only three microphones under the studied situations.

Knowing the geometry of a space is desirable for many applications, e.g. sound source localization, sound field reproduction or auralization. In circumstances where only acoustic signals can be obtained, estimating the geometry of a room is a challenging proposition. Existing methods have been proposed to reconstruct a room from the room impulse responses (RIRs). However, the sound source and microphones must be deployed in a feasible region of the room for it to work, which is impractical when the room is unknown. This work propose to employ a robot equipped with a sound source and four acoustic sensors, to follow a proposed path planning strategy to moves around the room to collect first image sources for room geometry estimation. The strategy can effectively drives the robot from a random initial location through the room so that the room geometry is guaranteed to be revealed. Effectiveness of the proposed approach is extensively validated in a synthetic environment, where the results obtained are highly promising.

In an active noise control (ANC) system, computational complexity is one major concern when designing practical control algorithms. For an ANC system with multiple secondary sources and error microphones, one approach to reducing computational complexity is to apply a decentralized control scheme rather than centralized approaches. A decentralized scheme attempts to control a number of small-size ANC subsystems independently. In this paper, we consider the decentralized control of a two-channel ANC system tackling a noise disturbance in the frequency domain, where each channel consists of one secondary source and one error microphone. We propose a decentralized control method that is able to achieve the same noise reduction performance as the centralized controller with guaranteed convergence. The key step in designing the control method is to properly shape the eigenvalues of a matrix that models the two-channel secondary paths for each frequency index.

Personal audio generates sound zones in a shared space to provide private and personalized listening experiences with minimized interference between consumers. Regularization has been commonly used to increase the robustness of such systems against potential perturbations in the sound reproduction. However, the performance is limited by the system geometry such as the number and location of the loudspeakers and controlled zones. This paper proposes a geometry optimization method to find the most geometrically robust approach for personal audio amongst all available candidate system placements. The proposed method aims to approach the most “natural” sound reproduction so that the solo control of the listening zone coincidently accompanies the preferred quiet zone. Being formulated in the SVD-based modal domain, the method is demonstrated by applications in three typical personal audio optimizations, i.e., the acoustic contrast control, the pressure matching, and the planarity control. Simulation results show that the proposed method can obtain the system geometry with better avoidance of “occlusion,” improved robustness to regularization, and improved broadband equalization.

Room geometry estimation from corresponding Room Impulse Responses (RIRs) has attracted much attention in recent years, and a key challenge is to find the first order image source locations from the RIRs under different environments. Unlike the existing approaches which require a priori knowledge of the room or require some ideal conditions, this paper proposes an intuitive geometrical method based on the acoustical image source model. The proposed approach does not need any a priori knowledge of the room, only the RIRs from one arbitrary source location to five arbitrary receiving locations. The first order image sources of the walls in a room are identified first, then the room geometry is estimated based on the wall locations using a geometrical approach. Simulations with 2D and 3D convex polyhedral rooms demonstrate the feasibility and the precision of the proposed approach is discussed.

Active noise control algorithms without secondary path modeling are very appealing because they can effectively overcome the problems caused by imperfect secondary path model. For broadband noise control, subband structures have been adopted in these kinds of algorithms, where the full-band adaptive filter coefficients are obtained from subband filter coefficients with weight transformation. However, the usually used two-direction search method suffers from slow convergence if the secondary path phase is close to ±90°. To deal with this problem, a four-direction search method has been proposed in frequency domain, which is sometimes too complicated for practical applications. In this paper, a simplified algorithm is proposed to reduce the implementation complexity. In the proposed algorithm, the delay of the secondary path is estimated first, and then the estimated delay is used in the delay compensation to reduce the number of subbands. Two reference signals are generated in each subband and two update directions are used. Only one subband reference signal and one update direction are selected to approximate the phase response of the residual secondary path, and then the full-band adaptive controller coefficients are adjusted directly in time domain with them without the need of further weight transformation. Just like those existing subband algorithms, the simplified algorithm can obtain similar steady-state noise reduction performance as that of the FxLMS algorithm but without secondary path modeling. Simulations and experiments are carried out to demonstrate the feasibility of the proposed algorithm.

Most algorithms for active impulsive noise control employ non‐linear transformations to limit the reference and/or error signals and to maintain system stability. From a more direct manner, a new cost function is proposed in this study which is defined as the summation of the squared Euclidean norm of difference between the currently updated filter coefficients vector and all past filter coefficients vector subject to the constraint imposed on the adaptive filter output. A new adaptive algorithm is derived from the cost function and called the filtered weight filtered‐ x normalised least mean square algorithm because it can be interpreted as a post filter structure which passes the filter coefficients through a first‐order infinite impulse response filter. The proposed algorithm is suitable for active control of impulsive noise since it directly limits the dynamic range of the adaptive filter coefficients and prevents heavy fluctuation of the filter coefficients. Simulations compare the performance of the proposed algorithm with the existing algorithms and demonstrate the effectiveness of the proposed algorithm.

To localize sound sources in room acoustic environments, time differences of arrival (TDOA) between two or more microphone signals must be determined. This problem is often referred to as time delay estimation (TDE). The multichannel cross-correlation-coefficient (MCCC) algorithm, which is an extension of the traditional cross-correlation method from two- to multiple-channel cases, exploits spatial information among multiple microphones to improve the robustness of TDE. In this paper, we propose a multichannel spatio-temporal prediction (MCSTP) algorithm, which can be viewed as a generalization of the MCCC principle from using only spatial information to using both spatial and temporal information. A recursive version of this new algorithm is then developed, which can achieve similar performance as MCSTP, but is computationally more efficient. Experimental results in reverberant and noisy environments demonstrate the advantages of this new method for TDE.

To overcome the limitations of the existing algorithms for active impulsive noise control, an algorithm based on minimizing the squared logarithmic transformation of the error signal is proposed in this correspondence. The proposed algorithm is more robust for impulsive noise control and does not need the parameter selection and thresholds estimation according to the noise characteristics. These are verified by theoretical analysis and numerical simulations.

This paper proposes a windowing frequency domain adaptive filter and an upsampling block transform preprocessing to solve the stereo acoustic echo cancellation problem. The proposed adaptive filter uses windowing functions with smooth cutoff property to reduce the spectral leakage during filter updating, so the utilization of the independent noise introduced by preprocessing in stereo acoustic echo cancellation can be increased. The proposed preprocessing is operated in short blocks with low processing delay, and it uses frequency-domain upsampling to meet the minimal block length requirement given by the band limit of simultaneous masking. Therefore, the simultaneous masking can be well utilized to improve the audio quality. The acoustic echo cancellation simulations and the audio quality evaluation show that, the proposed windowing frequency domain adaptive filter performs better than the conventional frequency domain adaptive filter in both mono and stereo cases, and the upsampling block transform preprocessing provides better audio quality and stereo acoustic echo cancellation performance than the half-wave preprocessing at the same noise level.

When implementing active noise control (ANC) systems, the filtered-x LMS (FXLMS) algorithm is often used, where the secondary path transfer function between the output of the adaptive control filter and the error sensor has to be modeled. To model the secondary path precisely, the secondary path estimation filter should be of sufficient order. However, too large an order increases the computation load of the FXLMS algorithm. In this letter, the relationship between the modeling phase error and the order of the secondary path modeling filter is analyzed.

Common active noise control (ANC) algorithms need to identify the secondary path transfer functions between the output of the adaptive control filters and the error sensors, and then use the information to guide the direction of control filter coefficient updating. Recently, Zhou et al. proposed an ANC algorithm without secondary path identification, and we improve their algorithm in this paper. For single-tone and narrowband noise control, the direction of control filter coefficient updating has four choices 180deg, 0deg, and plusmn90deg. We test the four update directions and select the one that works the best. If for all four update directions, the system converges slowly or diverges, we adjust the step size and test again with the new step size. The multitone and broadband noise control problems are converted into several single-tone and narrowband noise control problems by means of a frequency-domain delayless subband architecture. Compared to Zhou's algorithm, our proposed method yields good performance and converges quickly. Simulation results confirm the effectiveness of our proposed algorithm.

An efficient active noise control algorithm based on the delay less subband adaptive filter was previously proposed by Park (ldquoA delayless subband active noise control system for wideband noise control,IEEEtransactionsonspeechandaudioprocessing, vol. 9, no. 8, pp. 892-897, November 2001), whereby the computational complexity is reduced significantly by using a short impulse response filter to model the secondary path transfer function in a subband-decomposed form. In this correspondence, an overlap-save frequency-domain implementation of the algorithm is proposed. Simulations show that the proposed implementation has better performance than the original.

In some situations of active noise control, IIR filters are more suitable than FIR filters, due to the poles in the transfer function. A number of algorithms have been derived for applying IIR filters in active noise control (ANC), however, most of them use the direct form IIR filter structure, which faces the difficulties of checking stability and of relatively slow convergence speed for noise composed of narrow band components with large power disparity. To overcome these difficulties along with using the direct form IIR filters, a new adaptive algorithm is proposed; it uses and updates the lattice form adaptive IIR filter in an active noise control system. The comparison between the proposed algorithm and the commonly used filtered-u LMS and filtered-v LMS algorithms shows the superiority of the proposed algorithm.