Statistical model based SNR estimation method for speech signals


The performance of speech enhancement algorithms to a giant extent is connected to the employed signal-to-noise ratio (SNR) estimation techniques. Many of the prevailing SNR estimation techniques are based mostly on approaches that require either an experimentally pre-specified weighting factor or prior assumptions of the parameters in the signal model. In this reported work, a closed type SNRestimator is derived by modelling the noisy speech signal as a generalised traditional-Laplace distribution and estimating the variance of the signal and variance of the noise using high-order sample moments. The performance of the proposed technique is tested using real speech signals and compared with the well-known eigenvalue method.

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