Artificial Neural Network Based Identification of Multi-Operating-Point Impedance Model

The black-box impedance model of voltage source inverters (VSIs) can be measured at their terminals without access to internal control details, which greatly facilitate the analysis of inverter-grid interactions. However, the impedance model of VSI is dependent on its operating point and can have different profiles when the operating point is changed. This letter proposes a method for identifying the impedance model of VSI under a wide range of operating points. The approach is based on the artificial neural network (ANN), where a general framework for applying the ANN to identify the VSI impedance is established. The effectiveness of the ANN-based method is validated with the analytical impedance models

Quality Control in Remote Speech Data Collection

There is a need for algorithms that can automatically control the quality of the remotely collected speech databases by detecting potential outliers, which deserve further investigation. In this paper, a simple and effective approach for identification of outliers in a speech database is proposed. Using the deterministic minimum covariance determinant (DetMCD) algorithm to estimate the mean and covariance of the speech data in the mel-frequency cepstral domain, this approach identifies potential outliers based on the statistical distance of the observations in the feature space from the central location of the data that are larger than a predefined threshold. DetMCD is a computationally efficient algorithm, which provides a highly robust estimate of the mean and covariance of multivariate data even when 50% of the data are outliers. Experimental results using eight different speech databases with manually inserted outliers show the effectiveness of the proposed method for outlier detection in speech databases. Moreover, applying the proposed method to a remotely collected Parkinson's voice database shows that the outliers that are part of the database are detected with 97.4% accuracy, resulting in a significant decrease in the effort required for manually controlling the quality of the database

Robust Bayesian Pitch Tracking Based on the Harmonic Model

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. 1 1An implementation of the proposed algorithm using MATLAB may be found in https://tinyurl.com/yxn4a543

A New Virtual Tracking Sub-Algorithm Based Hybrid Active Control System for Narrowband Noise With Impulsive Interference

Mechanical noise is usually a mixture of narrowband and impulsive noise which needs complex active noise control (ANC) algorithms to improve the de-noising performance. But the ANC algorithm with a high computation load will reduce the real-time performance of an ANC system, thus decreasing the attenuation performance and even leading to divergence. To alleviate this contradiction in narrowband ANC systems, a new virtual filtered-x L0 norm discrete Fourier cancellation (FxL0DFC) based hybrid FxNLMS(filtered-x normalized least mean square)-FxDFC framework is proposed to decrease the total computing load and keep good attenuation performance. For a fast-changing noise, the FxNLMS algorithm is employed. The new virtual FxL0DFC algorithm serves to prepare parameters for steady-state, and when this happens, the FxDFC algorithm with the parameters provided by FxL0DFC is applied. Compared to using the FxNLMS algorithm to attenuate narrowband periodical noise, the FxDFC algorithm has nearly the same tracking performance while having a low computational load. As a result, the FxL0DFC-based FxNLMS-FxDFC algorithm leads to a reduction of the total computational load. Moreover, the proposed method performs excellently in terms of tracking in simulations and experiments on actual data, particularly in environments with rapid power changes and impulsive noise.</p

An Analysis of Traditional Noise Power Spectral Density Estimators Based on the Gaussian Stochastic Volatility Model

Many single- and multi-channel speech enhancement techniques, old and new, rely in one way or another on estimates of the noise power spectral density (PSD). For example, the classical Wiener filter requires that either the speech or noise PSD be estimated. Typically, the noise PSD is estimated, as it is often easier to model and estimate than the speech. As a result, much attention has been paid to this important problem over the past couple of decades, with important scientific milestones being the minimum statistics (MS), the minima controlled recursive averaging (IMCRA), and the minimum mean squared (MMSE) estimators. Despite leading to major progress, these estimators are rather ad hoc, making them difficult to tune and improve in a systematic manner. In this article, we analyse some of the common heuristics employed in such noise PSD estimators to put them on firmer mathematical ground. More specifically, we use the Gaussian stochastic volatility model and show that the MMSE noise PSD estimator can be interpreted as a special case thereof. Moreover, we analyze the related problem of speech presence probability (SPP) estimation and show that the SPP estimation performed in the MMSE noise PSD estimator can be interpreted as an SNR estimator in the context of the Gaussian stochastic volatility model.</p

CGMM-Based Sound Zone Generation Using Robust Pressure Matching With ATF Perturbation Constraints

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.</p