Method of noise reduction using correction vectors based on dynamic aspects of speech and noise normalization
First Claim
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;
selecting a mixture component of a trained model based at least in part on the input feature vector;
identifying a correction vector that incorporates dynamic aspects of a pattern signal based on the selected mixture component, the correction vector having at least one delta coefficient; and
adding the correction vector to the input feature vector to form a clean feature vector.
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Abstract
A method and apparatus are provided for reducing noise in a signal. Under one aspect of the invention, a correction vector is selected based on a noisy feature vector that represents a noisy signal. The selected correction vector incorporates dynamic aspects of pattern signals. The selected correction vector is then added to the noisy feature vector to produce a cleaned feature vector. In other aspects of the invention, a noise value is produced from an estimate of the noise in a noisy signal. The noise value is subtracted from a value representing a portion of the noisy signal to produce a noise-normalized value. The noise-normalized value is used to select a correction value that is added to the noise-normalized value to produce a cleaned noise-normalized value. The noise value is then added to the cleaned noise-normalized value to produce a cleaned value representing a portion of a cleaned signal.
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Citations
7 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; selecting a mixture component of a trained model based at least in part on the input feature vector; identifying a correction vector that incorporates dynamic aspects of a pattern signal based on the selected mixture component, the correction vector having at least one delta coefficient; and adding the correction vector to the input feature vector to form a clean feature vector. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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Specification