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A new scheme for the estimation of formant frequencies from noise-corrupted speech signals is presented in this paper. In order to overcome the effect of noise, first, instead of conventional autocorrelation function (ACF), a once-repeated ACF of the observed data is employed. A ramp cosine cepstrum model of the ORACF of speech signal is developed, followed(More)
In this paper, we propose a new algorithm for pitch estimation from speech signals heavily degraded by additive noise based on both time and frequency domain representations. A least-squares minimization technique is first developed for the accurate estimation of a pitch-harmonic (PH) wherein a harmonic sinusoidal model of clean speech is exploited as a(More)
Feedforward symbol timing recovery techniques are widely used in burst-mode transmission systems. In this paper, a timing estimation method that uses two samples/symbol sampling rate is investigated. We provide a theoretical justification for the algorithm and present the variance of the timing estimate for different parameters including roll-off factor and(More)
In this paper, a very efficient semiblind approach that uses a training-based least square criterion along with a blind constraint is proposed for multiple-input-multiple-output-orthogonal frequency-division multiplexing (MIMO-OFDM) channel estimation. The blind constraint is derived from the linear prediction of the received MIMO-OFDM signal and is used in(More)
This paper presents an algorithm for the parameter estimation of minimum-phase autoregressive moving average (ARMA) systems from noise-corrupted observations. In order to estimate the AR parameters of the ARMA system, an enhanced autocorrelation function (ACF) of the observed data is employed in a modified form of least-squares Yule-Walker equations. For(More)
This paper presents an approach for the identification of minimum-phase autoregressive moving average (ARMA) systems in the presence of additive noise. For the identification of the AR part of an ARMA system, unlike conventional correlation based methods, we propose to employ a once-repeated autocorrelation function (ORACF) of the observed noisy signal(More)
A new method for pitch estimation from noisy speech signals based on a pitch-harmonic extraction is presented in this paper. At first, a variable-length average magnitude difference function (VLAMDF) that is able to alleviate the adverse effects of intrinsic falling minima of the conventional AMDF, has been proposed. We argue that the fast Fourier transform(More)
In this paper, a new technique is proposed for the estimation of pitch from the noise-corrupted speech. To enhance speech in a noisy environment, a spectral subtraction (SS) based noise reduction scheme is incorporated prior to pitch estimation. The de-noised speech thus obtained is passed through an inverse filter, whose parameters are derived from the(More)
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