GAUSSIAN MIXTURE LINEAR PREDICTION
作者： Jouni Pohjalainen , Paavo Alku
DOI: 10.1109/ICASSP.2014.6854813
关键词: Artificial intelligence 、 Linear prediction 、 Estimation theory 、 Distortion 、 Signal processing 、 Pattern recognition 、 Background noise 、 Fourier transform 、 Autoregressive model 、 Mathematics 、 Gaussian
摘要: This work introduces an approach to linear predictive signal analysis utilizing a Gaussian mixture autoregressive model. By initializing different states of the model approximately correspond target and expected type undesired components, such as background noise, iterative parameter estimation converges towards focused prediction signal. Differently initialized trained variants are evaluated using objective spectrum distortion measures well in feature extraction for speech detection presence ambient noise. In these evaluations, novel methods perform better than Fourier transform conventional prediction.
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