The performance of personal sound systems is often degraded by inaccurate acoustic measurements. To achieve robust control while balancing acoustic contrast and signal distortion, this work proposes a robust hybrid optimization method that exploits both acoustic contrast control and pressure matching (ACC-PM). The method addresses perturbations caused by uncertainties in the acoustic transfer functions such as temperature changes, head movement, etc, modeled as norm-bounded uncertainties. Although the resulting worst-case optimization is inherently non-convex, it is reformulated as a second-order cone programming problem, which can be efficiently solved. Numerical simulations demonstrate the effectiveness of the proposed robust ACC-PM algorithm, showing an improvement over 18% in terms of AC compared to vanilla ACC-PM.

Existing literature on sound zone control generally consider the signal model to be linear. However, this is seldom true in practice owing to nonlinear distortions arising from the loudspeakers, especially in consumer applications. In this paper, we propose a new signal model for personal sound zone control that takes into consideration any nonlinear behaviour that may arise from the loudspeakers. Following the proposed signal model, the optimization problem is formulated such that it inherently ensures the reduction of nonlinear distortion effects in both the bright and dark zones. In addition, a broadband nonlinear acoustic contrast control - pressure matching approach is proposed for the new signal model. Simulation results on practical data show that our proposed approach can provide improvement in the acoustic contrast and/or the signal distortion performance compared to the traditional linear solution, in the presence of nonlinear distortions. Moreover, important observations are made for the study of nonlinear effects on sound zone control.

The disturbance of velocity sensor data may cause serious problems when the motor is running at high velocity. To tackle this issue, a velocity estimation method for high-speed linear induction motors (HS-LIM) is proposed in this paper. Firstly, the state equations of voltage, current, and velocity are established in a synchronous coordinate system to obtain the relationship between the voltage and the velocity. Secondly, the voltages of each stator section are used to synthesize the voltage characteristic waves, which are used to calculate the velocity. Finally, two velocity estimation values are fused into one more precise velocity data based on the Kalman filtering recursion algorithm. The simulation experiment verifies that the voltage characteristic waves can accurately estimate the mover velocity of HS-LIM, and the Kalman fusion velocity gets better precise.

This paper introduces five novel deep-learning architectures for speech enhancement. Existing methods typically use time-domain, time-frequency representations, or a hybrid approach. Recognizing the unique contributions of each domain to feature extraction and model design, this study investigates the integration of waveform and complex spectrogram models through cross-domain fusion to enhance speech feature learning and noise reduction, thereby improving speech quality. We examine both cascading and parallel configurations of waveform and complex spectrogram models to assess their effectiveness in speech enhancement. Additionally, we employ an orthogonal projection-based error decomposition technique and manage the inputs of individual sub-models to analyze factors affecting speech quality. The network is trained by optimizing three specific loss functions applied across all sub-models. Our experiments, using the DNS Challenge (ICASSP 2021) dataset, reveal that the proposed models surpass existing benchmarks in speech enhancement, offering superior speech quality and intelligibility. These results highlight the efficacy of our cross-domain fusion strategy.

The performance of sound zone control algorithms are known to degrade significantly with changes in acoustic conditions including perturbations of control microphones' positions. In this work, we study the feasibility and effectiveness of using low rank approximations of RIRs to calculate sound zone control filters, to improve the robustness of sound zone control algorithms to perturbations in the bright zone (BZ) microphones. For algorithm design, we consider the framework of variable span linear filter (VSLF) which allows a wide range of user selectivity between acoustic contrast (AC) and signal distortion (SD) trade off, including acoustic contrast control (ACC) and pressure matching (PM) methods as special cases. Detailed simulation study shows that above a certain rank of the variable span trade-off (VAST) filter, the proposed approach using low rank RIRs to derive the control filters provides higher AC compared to using full rank RIRs, when there are perturbations in BZ microphone positions.

The nonlinear characteristics of the thyristor and its real-time fault injection problem greatly challenge the hardware-in-the-loop (HIL) implementation of high-speed linear motors (HSLM) with power supply switching faults. In this paper, a real-time simulation model based on the improved associated discrete circuit switch model is proposed by analyzing the operating characteristics of the thyristor in healthy and faulty states. The entire segmented power supply system is constructed on an FPGA-based HIL platform using the fixed-point schematic to verify the correctness of the proposed model. The results of the offline simulation and HIL test demonstrate that the proposed model can precisely reflect the current asymmetry caused by the mutual asymmetry inductance of the stator windings after a power switching fault. The research can provide a test platform for fault diagnosis algorithms and control protection strategies for HSLM with segmented power supply systems.

The high-speed linear induction motor (LIM) has multiple parameters coupling and fast time-varying characteristics, which easily leads to current and thrust control instability and further restricts speed improvement. To address this issue, this paper proposes a decoupling mathematical modelling method to accurately characterize the time-varying parameter feature of LIMs. Subsequently, an indirect magnetic field orientation control strategy, consisting of the voltage feedforward control algorithm and a PID feedback control algorithm with time-varying parameters, is proposed based on the mathematical model. The simulation results demonstrate that the proposed method exhibits better stability in the fast changes of parameters, and the speed of the LIM is superior to the traditional methods.

Abstract Due to its self‐regularising nature and its ability to quantify uncertainty, the Bayesian approach has achieved excellent recovery performance across a wide range of sparse signal recovery applications. However, most existing methods are based on the real‐value signal model, with the complex‐value signal model rarely considered. Motivated by the adaptive least absolute shrinkage and selection operator (LASSO) and the sparse Bayesian learning framework, a hierarchical model with adaptive Laplace priors is proposed in this paper for recovery of complex sparse signals. Moreover, the space alternating approach is integrated into the algorithm to reduce the computational complexity of the proposed method. In experiments, the proposed algorithm is studied for complex Gaussian random dictionaries and different types of complex signals. These experiments show that the proposed algorithm offers better recovery performance for different types of complex signals than state‐of‐the‐art methods.

Personal sound zone (PSZ) refers to the technique that uses an array of loudspeakers and digital signal processing tools to achieve spatial soundfield control. To generate the target sound zones, this technique generally requires to know the acoustic transfer functions (ATFs) between the loudspeakers and the spots where soundfields are to be controlled. In practical applications, however, the true ATFs are never accessible and they have to be measured or estimated. Due to many sophisticated reasons, the measured ATFs generally deviate from the true ones, which may lead to significant degradation in performance of sound zone reproduction. In this work, a robust pressure matching (RPM) algorithm is presented for sound zone generation. It exploits a complex Gaussian mixture model (CGMM) to model the ATFs and their perturbations. The CGMM parameters are estimated using the expectation-maximization (EM) algorithm. To improve the robustness of the pressure matching method, an uncertainty constraint is applied to the ATF estimates and the pressure matching problem is then formulated as one of biconvex optimization. The coordinate descent algorithm is subsequently used to solve the optimization problem, thereby obtaining the optimal control filter. In comparison with the existing pressure matching methods without considering the effect of ATF perturbations, the presented algorithm is able to achieve lower normalized signal distortion energy and higher signal to interference ratio. Numerical simulations justify the effectiveness of the presented algorithm as well as its advantages over the traditional methods.

Personal sound field control techniques aim to produce sound fields for different sound contents in different places of an acoustic space without interference. The limitations of the state-of-the-art methods for sound field control include high latency and computational complexity, especially in the cases when the reverberation time is long and number of loudspeakers is large. In this paper, we propose a personal sound field control approach that exploits interframe correlation. Considering the past frames, the proposed method can accommodate long reverberation time with a low latency. To find the optimal parameters for the physical meaningful constraints, the subspace decomposition and Newton’s method are applied. Furthermore, a sound field distortion oriented subspace construction method is proposed to reduce the subspace dimension. Compared with traditional methods, simulation results show that the proposed algorithm is able to obtain a good trade-off between acoustic contrast and reproduction error with a low latency for measured room impulse responses.

Sound field control systems deployed in room acoustic environments require knowing the acoustic channel impulse responses between the loudspeakers and matching microphones, which are challenging to estimate accurately due to perturbations caused by such factors as temperature changes and sensors’ position mismatches. To deal with this issue, a robust pressure matching algorithm is developed in this work where a perturbation term of the acoustic transfer function (ATF) is modeled as a Gaussian process, based on which an uncertainty constraint is applied to limit the impact of perturbation on pressure matching. This constrained problem is formulated as one of biconvex optimization, and a coordinate descent algorithm is adopted to estimate the optimal control filter. Simulations are performed and results show that the proposed method is able to achieve more accurate control as compared to the standard pressure matching algorithm in the presence of ATF perturbations.

In the switching process of segmented power supply linear motor (SPSLM), the thyristor can cause the switching current impact and lead to insufficient utilization of converter capacity. To reduce this current impact, the mathematical model of linear motor concerning the parallel connection and zero-state response is deduced, the influence of different voltage switching angles on the current of the converter is analyzed, and then a switching method is proposed by calculating the position of the mover and voltage phase angle to minimize the effect of parallel connection and the zero-state response of stator. The electromagnetic transient simulation is implemented in Matlab/Simulink platform, the simulation results verify that the proposed method can reduce 50% of the current impact by comparing it with the conventional method.

The nonlinear feature of thyristor and asymmetry geometry of multiphase bring the great challenge to achieve real-time modeling of segmented multiphase linear motor (SMLM). In this paper, the physical characteristics of the thyristor and neutral point of the stator are analyzed to propose real-time modeling of SMLM switched by the thyristor. The pipelined and parallel structure of FPGA is introduced to reduce the hardware resource requirement of FPGA and accelerate the simulation speed. The HIL test platform based on Xilinx Virtex 7 series FPGA is built to verify the accuracy and efficiency. The results show that the proposed model can precisely reflect the nonlinear feature of the current zero-crossing shutdown of the thyristor without iteration when turning OFF and the zero-state response of the stator when turning ON. This research can be used for the hardware in the loop test of a high-speed linear motor propulsion system.

In this paper, we propose a novel non-negative matrix factorization (NMF) and hidden Markov model (NMF-HMM) based speech enhancement algorithm, which employs a Poisson mixture model (PMM). Compared to the previously proposed NMF-HMM method, the new algorithm, termed PMM-NMF-HMM, uses the Poisson mixture distribution for the state conditional likelihood function for a HMM rather than the single Poisson distribution. This means that there are the more basis matrices that can be used to model the speech and noise signals, so more signal information can be captured by the resulting model. The proposed method is supervised and thus includes a training and an enhancement stage. It is shown that, in the training stage, the proposed method can be implemented efficiently using multiplicative update (MU) for the model parameters, much like the NMF-HMM algorithm. In the speech enhancement stage, which can be performed online, a novel PMM-NMF-HMM minimum mean-square error (MMSE) estimator is developed. The experimental results indicate that the PMM-NMF-HMM method can obtain higher short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) score than NMF-HMM. Additionally, the method also outperforms other state-of-the-art NMF- based supervised speech enhancement algorithms.

The creation of sound zones with frequency-domain variable span trade-off filters (VAST) is investigated herein. Both narrowband and broadband discrete Fourier transform (DFT)-domain VAST approaches are proposed, and we discuss their relationship to the existing time-domain VAST approach. The core idea in VAST is to apply a generalized eigenvalue decomposition to the spatial statistics to control the trade-off between acoustic contrast and signal distortion. Moreover, a method for determining the optimal Lagrange multiplier that controls this trade-off is also considered in terms of physical, meaningful parameters. Through analysis and experiments, a performance comparison using measured room impulse responses is conducted not only between the two proposed methods but also between the two proposed methods and the existing time-domain approach. The results confirm that the broadband approach is able to transfer the acoustic contrast from one frequency bin to another, which is not the case for the narrowband approach. Furthermore, the results also show that the proposed DFT-domain VAST approach can be considered to be a special case of the time-domain VAST approach.

Personal sound zones provide users to experience independent listening and quiet areas in the same acoustic environment using multiple loudspeakers. The generalized eigenvalue decomposition (GEVD) has been proposed for sound zones generation, allowing user to control the trade-off between acoustic contrast and signal distortion by adjusting some parameters. Unfortunately, these parameters are not physically meaningful, and the user has to tune them for different source materials and acoustic environments. Moreover, performing a high dimensional GEVD is computational complex. In this article, we first propose various strategies to control the reproduced sound zones as precisely and accurately as possible by reformulating the problem using physically meaningful constraints using regularization approach. Then, a hybrid approach of combining the conjugate gradient method and GEVD is proposed to reduce the computational complexity and signal distortion when the subspace dimension is small. The proposed methods show precise control over the reproduced sound zone via extensive numerical simulations in reverberant environments for different physically meaningful constraints.

Sound zone control enables different users to enjoy different audio contents in the same acoustic environment. Generalized eigenvalue decomposition (GEVD)-based methods allow us to control the tradeoff between the acoustic contrast (AC) and signal distortion (SD). However, such methods have a high computational complexity. In this paper, we propose a fast reduced-rank sound zone control algorithm using the conjugate gradient (CG) method. Instead of using the eigenvectors as the basis for the solution space, the search directions in the CG method are used to reduce the computational complexity. Then, a low dimensional EVD is applied to obtain the sub-optimal control filter coefficients. The dark zone power can be adjusted by a parameter, which implicitly controls the trade-off between the AC and SD. Compared with GEVD-based methods, experimental results show that the proposed algorithm has a degradation of performance (4-5 dB) in terms of AC or SD but a high improvement on computational efficiency.

In this paper, we present a novel supervised Non-negative Matrix Factorization (NMF) speech enhancement method, which is based on Hidden Markov Model (HMM) and Kullback- Leibler (KL) divergence (NMF-HMM). Our algorithm applies theHMMto capture the timing information, so the temporal dynamics of speech signal can be considered by comparing with the traditional NMF-based speech enhancement method. More specifically, the sum of Poisson, leading to the KL divergence measure, is used as the observation model for each state of HMM. This ensures that the parameter update rule of the proposed algorithm is identical to the multiplicative update rule, which is quick and efficient. In the training stage, this update rule is applied to train the NMF-HMM model. In the online enhancement stage, a novel minimum mean-square error (MMSE) estimator that combines the NMF-HMM is proposed to conduct speech enhancement. The performance of the proposed algorithm is evaluated by perceptual evaluation of speech quality (PESQ) and short-timeobjective intelligibility (STOI). The experimental results indicate that the STOI score of proposed strategy is able to outperform 7% than current state-of-the-art NMF-based speech enhancement methods.

Fundamental frequency is one of the most important characteristics of speech and audio signals. Harmonic model-based fundamental frequency estimators offer a higher estimation accuracy and robustness against noise than the widely used autocorrelation-based methods. However, the traditional harmonic model-based estimators do not take the temporal smoothness of the fundamental frequency, the model order, and the voicing into account as they process each data segment independently. In this paper, a fully Bayesian fundamental frequency tracking algorithm based on the harmonic model and a first-order Markov process model is proposed. Smoothness priors are imposed on the fundamental frequencies, model orders, and voicing using first-order Markov process models. Using these Markov models, fundamental frequency estimation and voicing detection errors can be reduced. Using the harmonic model, the proposed fundamental frequency tracker has an improved robustness to noise. An analytical form of the likelihood function, which can be computed efficiently, is derived. Compared to the state-of-the-art neural network and nonparametric approaches, the proposed fundamental frequency tracking algorithm has superior performance in almost all investigated scenarios, especially in noisy conditions. For example, under 0 dB white Gaussian noise, the proposed algorithm reduces the mean absolute errors and gross errors by 15% and 20% on the Keele pitch database and 36% and 26% on sustained /a/ sounds from a database of Parkinson's disease voices. A MATLAB version of the proposed algorithm is made freely available for reproduction of the results.11An implementation of the proposed algorithm using MATLAB may be found in https://tinyurl.com/yxn4a543.

Pitch estimation is an important task in speech and audio analysis. In this paper, we present a multi-pitch estimation algorithm based on block sparse Bayesian learning and intra-block clustering for speech analysis. A statistical hierarchical model is formulated based on a pitch dictionary with a fixed maximum number of harmonics for all the candidate pitches. Block sparse Bayesian learning is proposed for estimating the complex amplitudes. To deal with the problem of unknown harmonic orders and subharmonic errors, intra-block clustering structured sparsity prior is also introduced. The statistical update formulas are obtained by the variational Bayesian inference. Compared with the conventional group LASSO-type algorithms for multi-pitch estimation, experimental results indicate robustness against noise and improved estimation accuracy of the proposed method.

In this paper, we present a speech analysis method based on sparse pole-zero modeling of speech. Instead of using the all-pole model to approximate the speech production filter, a pole-zero model is used for the combined effect of the vocal tract; radiation at the lips and the glottal pulse shape. Moreover, to consider the spiky excitation form of the pulse train during voiced speech, the modeling parameters and sparse residuals are estimated in an iterative fashion using a least 1-norm pole-zero with sparse deconvolution algorithm. Compared with the conventional two-stage least squares pole-zero, linear prediction and sparse linear prediction methods, experimental results show that the proposed speech analysis method has lower spectral distortion, higher reconstruction SNR and sparser residuals.

High frequency is an effective method to improve transmission efficiency of inductively coupled power transfer (ICPT) system. However, it is hard for the high capacity semiconductor device to meet the high frequency need. In this paper, a novel multiple-frequency inductively power transfer system is proposed. By adopting a novel multiple-frequency topology of inverter, the transfer frequency of the system could be several times the inverter's switching frequency. A doubled-frequency half bridge inverter is proposed in the paper, and it is analyzed and compared with the traditional one. Simulation and experiment results indicate that doubled-frequency inverter based inductively power transfer system can improve the transfer frequency effectively and the transmission efficiency is increased especially for high power application.

The long primary double sided linear induction motor (DSLIM) with segmented power supply is often used to increase power factor in electromagnetic acceleration applications. Some researchers analyzed the influence of end effect to thrust, but few focused on the thrust fluctuation in the block feeding change process. This paper establishes the mathematical model of DSLIM module and indicates the thrust fluctuation when the secondary enters a primary region or exits a primary region. A new field orientation formulation and the thrust expression are deduced in this paper. It shows that the thrust fluctuation is effectively reduced by increasing the slip frequency or optimizing the orientation. The simulation and experiment results verify the validity of the thrust fluctuation elimination method.

Because the numerous nonlinear components and fiber interfaces are used in Module Multiple Converter (MMC), the HIL test of MMC with detailed EMT model and the full-scale interface is hard to be implemented. In recent years, this existing disadvantage caused the MMC controller hardware and software difference between the HIL test and real project. In this paper, a detailed EMT model of hybrid MMC fitting for full-scale interface HIL test is proposed, and the equivalent arm circuit of hybrid MMC and the nonlinear feature of the sub-module are introduced. Then the full-scale interface HIL test system is built to test the performance of proposed model under various conditions. Comparing with the reference model built in MATLAB with detailed semiconductor model, the accuracy of the proposed method can meet the requirement of HIL tests, the current difference of two methods is less than 0.73% and the capacitor voltage difference is less than 0.39%. Due to the full-interface structure, the proposed method can simulate the divergence of capacitor voltage and all faults of the submodule. The MMC controller tested by the proposed method can be directly used in the real project without hardware and software modification.

A robust adaptive filtering algorithm based on the convex combination of two adaptive filters under the maximum correntropy criterion (MCC) is proposed. Compared with conventional minimum mean square error (MSE) criterion-based adaptive filtering algorithm, the MCC-based algorithm shows a better robustness against impulsive interference. However, its major drawback is the conflicting requirements between convergence speed and steady-state mean square error. In this letter, we use the convex combination method to overcome the tradeoff problem. Instead of minimizing the squared error to update the mixing parameter in conventional convex combination scheme, the method of maximizing the correntropy is introduced to make the proposed algorithm more robust against impulsive interference. Additionally, we report a novel weight transfer method to further improve the tracking performance. The good performance in terms of convergence rate and steady-state mean square error is demonstrated in plant identification scenarios that include impulsive interference and abrupt changes.