Method of noise reduction based on dynamic aspects of speech
First Claim
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1. A method for reducing noise in a noisy input signal, the method comprising:
- converting a frame of the noisy input signal into an input feature vector;
obtaining a static-based prediction for a noise-reduced feature vector using a prior model of static aspects of clean signals;
obtaining a dynamic-based prediction for the noise-reduced feature vector using a prior model of dynamic aspects of clean signals;
combining the static-based prediction and the dynamic-based prediction to form at least part of a combined prediction; and
multiplying the combined prediction by a measure of the probability of the input feature vector occurring to produce at least one component of the noise-reduced feature vector.
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Abstract
A system and method are provided that reduce noise in pattern recognition signals. To do this, embodiments of the present invention utilize a prior model of dynamic aspects of clean speech together with one or both of a prior model of static aspects of clean speech, and an acoustic model that indicates the relationship between clean speech, noisy speech and noise. In one embodiment, components of a noise-reduced feature vector are produced by forming a weighted sum of predicted values from the prior model of dynamic aspects of clean speech, the prior model of static aspects of clean speech and the acoustic-environmental model.
112 Citations
29 Claims
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1. A method for reducing noise in a noisy input signal, the method comprising:
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converting a frame of the noisy input signal into an input feature vector; obtaining a static-based prediction for a noise-reduced feature vector using a prior model of static aspects of clean signals; obtaining a dynamic-based prediction for the noise-reduced feature vector using a prior model of dynamic aspects of clean signals; combining the static-based prediction and the dynamic-based prediction to form at least part of a combined prediction; and multiplying the combined prediction by a measure of the probability of the input feature vector occurring to produce at least one component of the noise-reduced feature vector. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A computer-readable medium having computer-executable instructions for performing steps comprising:
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using a prior model of static aspects of clean speech to produce a static-based predicted value; using a prior model of dynamic aspects of clean speech to produce a dynamic-based predicted value; applying a noisy feature vector representing a frame of noisy speech and an estimate of the noise in the frame to an acoustic environment model to produce an acoustic environment-based predicted value wherein the acoustic environment model is based on a non-linear function that describes a relationship between a noisy feature vector, a clean feature vector, and a noise feature vector; and combining the static-based predicted value, the dynamic-based predicted value and the acoustic environment-based predicted value to form at least one component of a noise-reduced feature vector. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20)
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21. A computer-readable medium having computer-executable instructions for performing steps comprising:
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using a prior model of static aspects of clean speech to produce a static-based predicted value; using a prior model of dynamic aspects of clean speech to produce a dynamic-based predicted value; applying a noisy feature vector representing a frame of noisy speech to an acoustic environment model to produce an acoustic environment-based predicted value; and combining the static-based predicted value, the dynamic-based predicted value and the acoustic environment-based predicted value to form at least one component of a noise-reduced feature vector through steps comprising; applying separate weights to each of the static-based predicted value, the dynamic-based predicted value and the acoustic environment-based predicted value to form a weighted static-based value, a weighted dynamic-based value and a weighted acoustic environment-based value; and summing the weighted static-based value, the weighted dynamic-based value and the weighted acoustic environment-based value. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29)
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Specification