A minimum mean squared error estimator for single channel speaker separation
نویسندگان
چکیده
The problem of separating out the signals for multiple speakers from a single mixed recording has received considerable attention in recent times. Most current techniques are based on the principle of masking: in order the separate out the signal for any speaker, frequency components that are not believed to belong to that speaker are suppressed. The signals for the speaker is reconstructed from the partial spectral information that remains. In this paper we present a different kind of technique – one that attempts to estimate all spectral components for the desired speaker. Separated signals are derived from the complete spectral descriptions so obtained. Experiments show that this method results in superior reconstruction to masking based methods.
منابع مشابه
Multi-channel Algorithms for Improving Speech Recognition Accuracy in Adverse Environments
The focus of this thesis is on speech processing techniques for increasing robustness against additive noise and reverberation. In particular, single-channel and multi-channel algorithms will be presented. Among single-channel techniques, improvements of minimum mean squared error based approaches have been proposed. Techniques for gain function smoothing, and soft-decision originally proposed ...
متن کاملOn Phase Importance in Parameter Estimation for Single-Channel Source Separation
A single-channel source separation (SCSS) algorithm is targeted to estimate the underlying unknown signals from their single-channel recorded mixture. Current SCSS methods often neglect the phase information in their parameter estimation and use the noisy phase in the signal reconstruction stage. In this paper, we investigate the impact of phase information in the parameter estimation stage of ...
متن کاملEstimating a Bounded Normal Mean Relative to Squared Error Loss Function
Let be a random sample from a normal distribution with unknown mean and known variance The usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible estimator. In many practical situations, is known in advance to lie in an interval, say for some In this case, the maximum likelihood estimator...
متن کامل