A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is
Gaussian mixture linear prediction
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
This work introduces an approach to linear predictive signal analysis utilizing a Gaussian mixture autoregressive model. By initializing different autoregressive states of the model to approximately correspond to the target signal and the expected type of undesired signal components, such as background noise, the iterative parameter estimation converges towards a focused linear prediction model of the target signal. Differently initialized and trained variants of mixture linear prediction aredoi:10.1109/icassp.2014.6854813 dblp:conf/icassp/PohjalainenA14a fatcat:ceopxp5kb5aulmcsqjcjmft2ay