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This paper presents a new method for voice activity detection (VAD) based on the autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) model. The speech signal is modeled as an AR-GARCH process in the time domain, and the likelihood ratio is computed and compared to a threshold. The time-varying variance of the speech signal(More)
Voice activity detection has attracted significant research efforts in the last two decades. Despite much progress in designing voice activity detectors, voice activity detection (VAD) in presence of transient noise is a challenging problem. In this paper, we develop a novel VAD algorithm based on spectral clustering methods. We propose a VAD technique(More)
ARCH and GARCH models have been used recently in model-based signal processing applications, such as speech and sonar signal processing. In these applications, additive noise is often inevitable. Conventional methods for parameter estimation of ARCH and GARCH processes assume that the data are clean. The parameter estimation performance degrades greatly(More)
This paper discusses the constrained two stage least squares (CLS2) estimator of the parameters of ARCH models under known order. This estimator is a modified version of the two stage least squares (TSLS) estimation. The estimator is easy to obtain and fast since it involves only quadratic optimization. At the same time, the estimator has the same(More)
Keywords: Sampling theorem Homogenous manifolds Band-limited functions Laplace–Beltrami operator a b s t r a c t In this paper, we address the problem of function extension when the available data lies on a homogeneous manifold (i.e. the domain of the function is a homogeneous manifold embedded in the Euclidean space) and the function is band-limited. We(More)
Image anomaly detection is the process of extracting a small number of clustered pixels which are different from the background. The type of image, its characteristics and the type of anomalies depend on the application at hand. In this paper, we introduce a new statistical model called noncausal autoregressive–autoregressive conditional heterosce-dasticity(More)
Voice activity detection (VAD) has attracted significant research efforts in the last two decades. Despite much progress in designing voice activity detectors, voice activity detection in presence of transient noise and low SNR is a challenging problem. In this paper, we propose a new VAD algorithm based on supervised learning. Our method employs Laplacian(More)
In this paper the student-t autoregressive conditional heteroscedasticity (ARCH) model is considered and a new estimator for the coefficients of the ARCH model and the degree of freedom of the Student-t noise is proposed. ARCH models have been used in numerous applications to model the unpredictability and the strong dependence of the instantaneous(More)