Abstract: A system and method reduces noise in a time series signal. A primary signal including stationary and non-stationary noise is modeled by a dynamic system having a continuum of states. A secondary signal including time series data is added to the primary signal to form a combined signal. The generic noise in the combined signal is estimated from samples of the combined signal using the dynamic system modeling the generic noise. Then, the estimated generic noise is removed from the combined signal to recover time series data.

Abstract: A method classifies data into multiple classes so that the data in each class have a class-conditional probability distribution. The class-conditional probability distributions of measured data are projected into a likelihood space. The projected class-conditional probability distributions in the likelihood space are then classified according to a discriminant classifier in likelihood space.

Abstract: A system and method reduces noise in a time series signal. A primary signal including stationary and non-stationary noise is modeled by a dynamic system having a continuum of states. A secondary signal including time series data is added to the primary signal to form a combined signal. The generic noise in the combined signal is estimated from samples of the combined signal using the dynamic system modeling the generic noise. Then, the estimated generic noise is removed from the combined signal to recover time series data.

Abstract: A method segments an audio signal including frames into non-speech and speech segments. First, high-dimensional spectral features are extracted from the audio signal. The high-dimensional features are then projected non-linearly to low-dimensional features that are subsequently averaged using a sliding window and weighted averages. A linear discriminant is applied to the averaged low-dimensional features to determine a threshold separating the low-dimensional features. The linear discriminant can be determined from a Gaussian mixture or a polynomial applied to a bi-model histogram distribution of the low-dimensional features. Then, the threshold can be used to classify the frames into either non-speech or speech segments. Speech segments having a very short duration can be discarded, and the longer speech segments can be further extended. In batch-mode or real-time the threshold can be updated continuously.

Abstract: A method extracts speech recognition features from a speech signal coded as a bitstream. First, the bitstream is decoded to recover linear predictive coding filter parameters, and a residual signal. Then, the linear predictive coding filter parameters and the residual signal are discriminatively combined into speech recognition features.