Method of noise estimation using incremental bayes learning
View Patent ↗A method and apparatus estimate additive noise in a noisy signal using incremental Bayes learning, where a time-varying noise prior distribution is assumed and hyperparameters (mean and variance) are updated recursively using an approximation for posterior computed at the preceding time step. The additive noise in time domain is represented in the log-spectrum or cepstrum domain before applying incremental Bayes learning. The results of both the mean and variance estimates for the noise for each of separate frames are used to perform speech feature enhancement in the same log-spectrum or cepstrum domain.
1. A method for estimating noise in a noisy signal, the method comprising:
dividing the noisy signal into frames; and
determining a noise estimate, including both a mean and a variance, for a frame using incremental Bayes learning, where a time-varying noise prior distribution is assumed and a noise estimate is updated recursively using an approximation for posterior noise computed at a preceding frame,
wherein determining a noise estimate comprises:
determining a noise estimate for a first frame of the noisy signal using an approximation for posterior noise computed at a preceding frame;
determining a data likelihood estimate for a second frame of the noisy signal; and
using the data likelihood estimate for the second frame and the noise estimate for the first frame to determine a noise estimate for the second frame.
2. The method of claim 1 wherein determining the data likelihood estimate for the second frame comprises using the data likelihood estimate for the second frame in an equation that is based in part on a definition of the noisy signal as a non-linear function of a clean signal and a noise signal.
3. The method of claim 2 wherein the equation is further based on an approximation to the non-linear function.
4. The method of claim 3 wherein the approximation equals the non-linear function at a point defined in part by the noise estimate for the first frame.
5. The method of claim 4 wherein the approximation is a Taylor series expansion.
6. The method of claim 5 wherein the approximation further comprises taking a Laplace approximation.
7. The method of claim 1 wherein using the data likelihood estimate for the second frame comprises using the noise estimate for the first frame as an expansion point for a Taylor series expansion of a non-linear function.
8. The method of claim 1 wherein using an approximation for posterior noise comprises using a Gaussian approximation.
9. The method of claim 1 wherein each noise estimate is based on a Gaussian approximation.
10. The method of claim 9 wherein determining the noise estimate comprises determining a noise estimate for each frame successively.