S. Chandra Sekhar

Indian Institute of Science, Bengalore, State of Karnataka, India

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Publications (8)7.44 Total impact

  • INTERSPEECH 2007, 8th Annual Conference of the International Speech Communication Association, Antwerp, Belgium, August 27-31, 2007; 01/2007
  • S. Chandra Sekhar, T.V. Sreenivas
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    ABSTRACT: We consider the problem of estimation of the signal-to-noise ratio (SNR) of an unknown deterministic complex phase signal in additive complex white Gaussian noise. The phase of the signal is arbitrary and is not assumed to be known a priori unlike many SNR estimation methods that assume phase synchronization. We show that the moments of the complex sequences exhibit useful mean-ergodicity properties enabling a “method-of-moments” (MoM)-SNR estimator. The Cramer–Rao bounds (CRBs) on the signal power, noise variance and logarithmic-SNR are derived. We conduct experiments to study the efficiency of the SNR estimator. We show that the estimator exhibits finite sample super-efficiency/inefficiency and asymptotic efficiency, depending on the choice of the parameters. At SNR, the mean square error in log-SNR estimation is approximately . The main feature of the MoM estimator is that it does not require the instantaneous phase/frequency of the signal, a priori. Infact, the SNR estimator can be used to track the instantaneous frequency (IF) of the phase signal. Using the adaptive pseudo-Wigner–Ville distribution technique, the IF estimation accuracy is the same as that obtained with perfect SNR knowledge and 8–10 dB better compared to the median-based SNR estimator.
    Signal Processing. 01/2006;
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    S.C. Sekhar, T.V. Sreenivas
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    ABSTRACT: We address the problem of estimating the instantaneous frequency (IF) of a phase signal using its level-crossing (LC) information based on front-end auditory processing motivation. We show that the problem of IF estimation using LC information can be cast in the framework of estimation from irregularly sampled data. The formulation has the generality of estimating different types of IF without the need for a quasistationary assumption. We consider two types of IF-polynomial and bandlimited; we use polynomial interpolating functions for the former, and for the latter, we propose a novel "line plus sum of sines" model. The model parameters are estimated by linear regression. Considering the noisy case, LC data for different levels is analyzed, and methods for combining different estimators from LCs are discussed. Theoretical and extensive simulation results show that the performance of the zero-crossing (ZC) based IF estimator and the level-crossing based IF estimator with smaller level values is better than those obtained with higher level values or their combinations. The new technique reaches the Crame´r-Rao bound (CRB) roughly above 4 dB signal-to-noise ratio (SNR), and its performance does not deteriorate rapidly with mismatch in the IF order compared with the other techniques in the literature.
    IEEE Transactions on Signal Processing 05/2005; · 2.81 Impact Factor
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    S.C. Sekhar, T.V. Sreenivas
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    ABSTRACT: We present a new zero-crossing based algorithm for decomposing a bandpass signal into the amplitude modulation (AM) and frequency modulation (FM) components. In this sequential algorithm, the FM component is first estimated using zero-crossing instant information in a k-nearest neighbour (k-NN) framework. The AM component is estimated by coherent demodulation using a time-varying lowpass filter that uses the estimated instantaneous frequency. Simulation results show that the proposed algorithm gives more accurate envelope and frequency estimates compared to the discrete-energy separation algorithm (DESA) which uses the Teager energy operator. Using the proposed approach on bandpass filtered speech and music, we can extract the fine-structured modulations that occur on a micro-time scale, within an analysis frame.
    Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on; 06/2004 · 4.63 Impact Factor
  • S. Chandra Sekhar, T. V. Sreenivas
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    ABSTRACT: The peak of the polynomial Wigner–Ville distribution (PWVD) can be used for estimating the instantaneous frequency (IF) of monocomponent polynomial phase signals. However, the PWVD kernel, optimized to yield a time–frequency distribution (TFD) localized along the IF, comprises of fractional-time-sampled signals. When implemented in a discrete-time scenario, this calls for signal interpolation. We study three interpolation schemes—linear, cubic polynomial and sinc and derive expressions for the variance of the interpolated samples in the presence of noise. In representing nonstationary signals using the PWVD, the instantaneous energy content of noise auto-terms and signal-noise cross-terms is found to be the least for linear interpolation scheme. For polynomial IF estimation using the peak of the PWVD, it was found that linear interpolation is a computationally efficient way of obtaining reasonably good estimates at low signal-to-noise ratios (SNRs). For high SNRs, sinc interpolation outperforms the other two schemes. Similar results were found when the experiment was extended to sinusoidal IF signals also.
    Signal Processing. 01/2004; 84(1):107-116.
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    T. V. Sreenivas, S. Chandra Sekhar
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    ABSTRACT: We address the problem of estimating instantaneous frequency (IF) of a real-valued constant amplitude time-varying sinusoid. Estimation of polynomial IF is formulated using the zero-crossings of the signal. We propose an algorithm to estimate nonpolynomial IF by local approximation using a low-order polynomial, over a short segment of the signal. This involves the choice of window length to minimize the mean square error (MSE). The optimal window length found by directly minimizing the MSE is a function of the higher-order derivatives of the IF which are not available a priori. However, an optimum solution is formulated using an adaptive window technique based on the concept of intersection of confidence intervals. The adaptive algorithm enables minimum MSE-IF (MMSE-IF) estimation without requiring a priori information about the IF. Simulation results show that the adaptive window zero-crossing-based IF estimation method is superior to fixed window methods and is also better than adaptive spectrogram and adaptive Wigner-Ville distribution (WVD)-based IF estimators for different signal-to-noise ratio (SNR).
    EURASIP Journal on Advances in Signal Processing. 01/2004;
  • S. Chandra Sekhar, T. V. Sreenivas
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    ABSTRACT: To estimate the instantaneous frequency (IF) using the peak of the spectrogram, we use an approach that automatically adapts the window length to the changes in IF and tracks it better than a fixed window approach. An adaptive window-based time–frequency representation is more useful for tracking events in time and frequency. The peak of the spectrogram obtained using the adaptive window length algorithm is used as an IF estimator and its performance in the presence of multiplicative and additive noise is studied. The performance is compared with that of pseudo-Wigner–Ville distribution (Ps.WVD). Both analytically and experimentally, adaptive spectrogram was found to be more robust than adaptive Ps.WVD.
    Signal Processing. 01/2003; 83(7):1529-1543.
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    S Chandra Sekhar, Sridhar Pilli, Tv Sreenivas
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    ABSTRACT: We address the problem of estimating the fundamental frequency of voiced speech. We present a novel solution motivated by the importance of amplitude modulation in sound processing and speech perception. The new algo-rithm is based on a cumulative spectrum computed from the temporal envelope of various subbands. We provide theoretical analysis to derive the new pitch estimator based on the temporal envelope of the bandpass speech signal. We report extensive experimental performance for synthetic as well as natural vowels for both real-world noisy and noise-free data. Experimental results show that the new technique performs accurate pitch es-timation and is robust to noise. We also show that the technique is superior to the autocorrelation technique for pitch estimation.