Removing noise from speech

A computer-readable medium having stored thereon computer-executable instructions which, when executed by a computer, cause the computer to:

• receive the digital speech waveform having the noise contained therein;
• segment the digital speech waveform into one or more frames, each frame having a clean portion and a noisy portion represented in a log power spectra domain;
• extract a feature component from each frame;
• create a nonlinear speech distortion model from the feature components;
• create a statistical noise model by making a Piecewise Linear Approximation (PLA) of the nonlinear speech distortion model to derive one or more terms in an Expectation-Maximization (EM) algorithm;
• determine the clean portion of each frame using the statistical noise model, a log power spectra of each frame, and a Gaussian Mixture Model (GMM) model of a digital speech waveform recorded in a noise controlled environment;
• and
• construct a clean digital speech waveform from each clean portion of each frame.

A computer system, comprising:

• a processor;
• and
• a memory comprising program instructions executable by the processor to:
• receive the digital speech waveform having the noise contained therein;
• segment the digital speech waveform into one or more frames, each frame having 32 milliseconds of speech, being positioned such that two consecutive frames half over-laps each other, and each frame having a clean portion and a noisy portion and the frames;
• extract a feature component from each frame;
• create a nonlinear speech distortion model from the feature components;
• create a statistical noise model by making a Piecewise Linear Approximation (PLA) of the nonlinear speech distortion model;
• determine the clean portion of each frame using the statistical noise model, a log power spectra of each frame, and a model of a digital speech waveform recorded in a noise controlled environment;
• and
• construct a clean digital speech waveform from each clean portion of each frame.