Noise estimation apparatus of obtaining suitable estimated value about sub-band noise power and noise estimating method

摘要

A noise estimation apparatus of estimating a noise in an input signal includes a sub-band noise estimator estimating a noise in a sub-band input signal, obtained by dividing the input signal by sub-bands. The sub-band noise estimator includes a power calculator calculating a sub-band input power of the sub-band input signal; a probability model holder holding information on probability model; and an a posteriori probability maximizer calculating an instantaneous estimated value of a sub-band noise power based on the sub-band input power, an estimated value of the sub-band noise power and the information on the probability model, so as to maximize a posteriori probability of the sub-band noise power. The information on the probability model includes a likelihood function regarding a posteriori signal-to-noise ratio (SNR) in dependence upon predictive a posteriori SNR; and a priori probability of the a posteriori SNR under a condition establishing averaged a posteriori SNR.

主权项

1. A noise estimation apparatus of estimating a noise included in an input signal, comprising:
at least one sub-band noise estimator estimating a noise included in a sub-band input signal, obtained by dividing the input signal by sub-bands; wherein said sub-band noise estimator comprises: a power calculator calculating a sub-band input power of the sub-band input signal; a probability model holder holding information on probability model obtained by modelizing stationarity of the noise; and an a posteriori probability maximizer calculating an instantaneous estimated value of a sub-band noise power on a basis of the sub-band input power, an estimated value of the sub-band noise power outputted from said sub-band noise estimator and the information on the probability model held in said probability model holder, so as to maximize a posteriori probability of the sub-band noise power, and wherein the information on the probability model includes information on: a likelihood function with regard to a posteriori signal-to-noise ratio (SNR) on a basis of a predictive a posteriori SNR; and a priori probability of the a posteriori SNR under a condition where averaged a posteriori SNR is established.