This article studies the stability issue of networked switched systems (NSSs) under denial-of-service (DoS) attacks. To address this issue, the derived limitations imposed on both the frequency of DoS attacks on each subsystem and the upper limit of attack duration that each subsystem can tolerate are mode-dependent, which is more efficient and flexible than the current results for NSSs. Moreover, we reveal the relationship between the upper bound of the average maximum tolerable attack duration associated with the corresponding subsystem and the actual mode-dependent average dwell time. Furthermore, we identify that the total tolerable DoS attack duration as a percentage of the system runtime in this article can be higher than existing results. Finally, an example is given to demonstrate the effectiveness of our work.

Despite of its wide success in many distributed statistical learning applications, the well-known Gaussian belief propagation (BP) algorithm still lacks sufficient understanding at the theoretical level. This paper studies the convergence of Gaussian BP by analyzing the dynamic behaviour of the marginal covariances. We show, under a mild technical assumption, that the information matrices (i.e., the inverses of marginal covariances) are guaranteed to converge exponentially to positive-definite matrices. The convergence rate is explicitly characterized. This result is a key step to the understanding of the dynamic behaviour of the BP iterations.

Head-related transfer functions (HRTFs) describe the acoustic filtering of incoming sounds by the human morphology and are essential for listeners to localize sound sources in virtual auditory displays. Since rendering complex virtual scenes is computationally demanding, we propose four algorithms for efficiently representing HRTFs in subbands, i.e., as an analysis filterbank (FB) followed by a transfer matrix and a synthesis FB. All four algorithms use sparse approximation procedures to minimize the computational complexity while maintaining perceptually relevant HRTF properties. The first two algorithms separately optimize the complexity of the transfer matrix associated to each HRTF for fixed FBs. The other two algorithms jointly optimize the FBs and transfer matrices for complete HRTF sets by two variants. The first variant aims at minimizing the complexity of the transfer matrices, while the second one does it for the FBs. Numerical experiments investigate the latency-complexity trade-off and show that the proposed methods offer significant computational savings when compared with other available approaches. Psychoacoustic localization experiments were modeled and conducted to find a reasonable approximation tolerance so that no significant localization performance degradation was introduced by the subband representation.

The inverse fast Fourier transform (IFFT) method is a time-frequency technique which was proposed to alleviate the complexity of the additive sound synthesis method in real-time applications. However, its application is limited by its inherent tradeoff between time and frequency resolutions, which are determined by the number of frequencies used for time-frequency processing. In a previous work, the authors proposed a frequency-refining technique for overcoming this frequency limitation, permitting achieving any time and frequency resolution using a small number of frequencies. In this correspondence we extend this work, by proposing a time-refining technique which permits overcoming the time resolution limitation for a given number of frequencies. Additionally, we propose an alternative to the frequency-refining technique proposed in our previous work, which requires about half the computations. The combination of these two results permits achieving any time and frequency resolution for any given number of frequencies. Using this property, we find the number of frequencies which minimizes the overall complexity. We do so considering two different application scenarios (i.e., offline sound design and online real-time synthesis). This results in a major complexity reduction in comparison with the design proposed in our previous work.

In this paper we investigate the use of formant and anti formant measurements of nasal consonants for speaker verification. The features are obtained using a pole-zero vocal tract model estimate optimized by minimizing a logarithmic criterion which is motivated by the perception of amplitude by the human auditory system. A GMM-UBM approach is used for performing speaker comparisons within the likelihood-ratio framework. Results are compared with systems based on Mel Frequency Cepstral Coefficients (MFCCs) as well as formant center frequencies and bandwidths obtained using the Snack Toolkit. The formant and anti-formant based system attains comparable results to the MFCC system and outperforms the formant-based approach while offering a more straight for ward interpretation in terms of a physical speech production model.

This paper studies system identification of ARMA models whose outputs are subject to finite-level quantization and random packet dropouts. A simple adaptive quantizer and the corresponding recursive identification algorithm are proposed and shown to be optimal in the sense of asymptotically achieving the minimum mean square estimation error. The joint effects of finite-level quantization and random packet dropouts on identification accuracy are exactly quantified. The theoretic results are verified by simulations.

The inverse fast Fourier transform (FFT) method was proposed to alleviate the computational complexity of the additive sound synthesis method in real-time applications, and consists in synthesizing overlapping blocks of samples in the frequency domain. However, its application is limited by its inherent tradeoff between time and frequency resolution. In this paper, we propose an alternative to the inverse FFT method for synthesizing colored noise. The proposed approach uses subband signal processing to generate time-frequency noise with an autocorrelation function such that the noise obtained after converting it to time domain has the desired power spectral density. We show that the inverse FFT method can be interpreted as a particular case of the proposed method, and therefore, the latter offers some extra design flexibility. Exploiting this property, we present experimental results showing that the proposed method can offer a better tradeoff between time and frequency resolution, at the expense of some extra computations.

A speech production model consists of a linear, slowly time-varying filter. Pole-zero models are required for a good representation of certain types of speech sounds, like nasals and laterals. From a perceptual point of view, designing them by minimizing a logarithmic criterion appears as a very suitable approach. The most accurate available results are obtained by using Newton-like search algorithms to optimize pole and zero positions, or the coefficients of a decomposition into quadratic factors. In this paper, we propose to optimize the numerator and denominator coefficients instead. Experimental results show that this is the computationally most efficient approach, especially when the optimization criterion considers a psychoacoustical frequency scale. To illustrate its applicability in speech processing, we used the proposed method for formant and anti-formant tracking as well as speech resynthesis.

The hybrid filterbank architecture permits implementing accurate, high speed analog-to-digital converters. However, its design is technically involved since perfect reconstruction of the desired samples cannot be achieved in general. In this paper we propose a design method which generalizes existing approaches by dropping the bandlimited assumption on the input signal. The design minimizes the power of the reconstruction error in the samples, for a given input signal power spectrum. We discuss the use of blind techniques to estimate the analysis filterbank parameters as well as the input power spectrum, and we present simulation results to demonstrate the clear advantage of the proposed design, even under input spectrum uncertainties.

A linear system can be approximated in the time-frequency domain by the composition of an analysis filterbank, a transfer matrix (subband model) and a synthesis filterbank, a method known as subband technique. In this paper we propose an iterative method to jointly optimize the subband model, analysis and synthesis filterbank. To this end we propose a minimization criterion which we solve using the so-called alternating least-squares method. As a possible application we consider the implementation of the so-called head related transfer functions which are used in virtual acoustics. Simulation results suggest that the subband technique, optimized using the proposed method proposed method, is a promising approach.

Both direct and indirect methods exist for continuous-time system identification. A direct method estimates continuous-time input and output signals from their samples and then use them to obtain a continuous-time model, whereas an indirect method estimates a discrete-time model first. Both methods rely on fast sampling to ensure good accuracy. In this paper, we propose a more direct method where a continuous-time model is directly fitted to the available samples. This method produces an exact model asymptotically, modulo some aliasing ambiguity, even when the sampling rate is relatively low.

Cyclic prefix (CP) is commonly used for channel equalization of discrete multitone (DMT) and orthogonal frequency-division multiplexing (OFDM) systems. This is often done in conjunction with a time-domain equalizer (TEQ) for reducing the capacity overhead caused by the CP. However, the use of TEQ greatly increases the computational cost, and is unable to eliminate the need for the CP. In this paper, we propose a subband approach to channel estimation and channel equalization for DMT and OFDM systems. This approach involves splitting the received signals into a number of frequency bands (called subbands), and estimating a constant parameter in each subband. The subband approach is conceptually simple, requires no CP, is much more numerically efficient than the TEQ method, and gives compatible or better estimation errors than the CP-based methods.

For stochastic analysis of single-rate linear systems, a desirable property for stochastic signals is ergodicity in the mean and correlation. Unfortunately, as we show, the ergodicity property may not be preserved under downsampling and uniformly stable linear filtering. This poses a serious problem for stochastic analysis of multirate linear systems. We introduce the notion of strong ergodicity which is preserved under a number of important multirate operations including downsampling, upsampling and time-variant uniformly stable linear filtering. We provide conditions for stochastic processes to be strongly ergodic. Using this result, we show that both independent random processes and bounded deterministic signals are strongly ergodic in the mean and correlation.

In this paper we propose two equalization methods for orthogonal frequency-division multiplexing (OFDM) modulation. These methods are intended to be alternatives to the known cyclic prefix equalization (CPE) method, and are derived using ideas from subband identification. The first proposed method is called subband equalization (SBE) and the second one is called enhanced cyclic prefix equalization (ECPE). The performance of different methods is compared in terms of bit error rate (BER) after equalization, convergence rate and computational cost in the presence of noise and fast fading. Both SBE and ECPE outperform the CPE in terms of BER and convergence rate. The SBE does not require the use of CP, and therefore saves channel capacity, but needs more computation than CPE, ECPE requires the use of CP, but its cost is compatible to that of CPE.

We study the problem of filterbank design for the subband identification method in the oversampled case. We aim to design the filterbanks such that the coupling between adjacent subbands is minimized. This allows the subband model of the system to be diagonal, thus simplifying the complexity of identification. Solutions are given to two minimization criteria: the worst-input case and white input case.