Thippur V. Sreenivas

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Compressive sensing (CS) has been proposed for signals with spar-sity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production , for recovering compressive sensed speech. Since the linear transform is signal dependent(More)
Considering a general linear model of signal degradation, by model-ing the probability density function (PDF) of the clean signal using a Gaussian mixture model (GMM) and additive noise by a Gaus-sian PDF, we derive the minimum mean square error (MMSE) es-timator. The derived MMSE estimator is non-linear and the linear MMSE estimator is shown to be a(More)
Speech and Audio processing techniques are used along with statistical pattern recognition principles to solve the problem of music instrument recognition. Non temporal, frame level features only are used so that the proposed system is scalable from the isolated notes to the solo instrumental phrases scenario without the need for temporal segmen-tation of(More)
—For vector quantization (VQ) of speech line spectrum frequency (LSF) parameters, we experimentally determine a mapping function between the mean square error (MSE) measure and the perceptually motivated average spectral distortion (SD) measure. Using the mapping function, we estimate the minimum bits/vector required for transparent quantization of(More)