Saman Mousazadeh

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—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)
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)
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, we propose a speech enhancement algorithm for estimating the clean speech using samples of air-conducted and bone-conducted speech signals. We introduce a model in a supervised learning framework by approximating a mapping from concatenation of noisy air-conducted and bone-conducted speech to clean speech in the short time Fourier transform(More)
In this paper, we propose a new method based on particle filters for maximum likelihood (ML) estimation of the parameters of autoregressive conditional heteroscedasticity (ARCH) and generalized autoregressive conditional heteroscedasticity (GARCH) models. Our method is based on gradient descend method and active set method for maximizing the likelihood(More)
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