Signature noise removal
First Claim
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1. A noise detection system, comprising:
- a computer memory that stores a noise model that includes spectral and temporal shape characteristics of a noise; and
a processor coupled with the computer memory;
where the processor is configured to access the noise model from the computer memory and analyze a signal to determine whether characteristics of the signal correspond to characteristics of the noise model;
where the processor is configured to fit the noise model to the signal in a time-frequency domain to evaluate spectral and temporal shape characteristics of a sound event in the signal;
where the processor is configured to identify the sound event as a noise event based on a correlation between the noise model and a signal envelope of the sound event; and
where the processor is configured to model individual sound events that make up the noise of the noise model, and model a temporal space between the individual sound events.
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Abstract
A speech enhancement system improves the perceptual quality of a processed voice signal. The system improves the perceptual quality of a voice signal by removing unwanted noise components from a voice signal. The system removes undesirable signals that may result in the loss of information. The system receives and analyzes signals to determine whether an undesired random or persistent signal corresponds to one or more modeled noises. When one or more noise components are detected, the noise components are substantially removed or dampened from the signal to provide a less noisy voice signal.
142 Citations
17 Claims
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1. A noise detection system, comprising:
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a computer memory that stores a noise model that includes spectral and temporal shape characteristics of a noise; and a processor coupled with the computer memory; where the processor is configured to access the noise model from the computer memory and analyze a signal to determine whether characteristics of the signal correspond to characteristics of the noise model; where the processor is configured to fit the noise model to the signal in a time-frequency domain to evaluate spectral and temporal shape characteristics of a sound event in the signal; where the processor is configured to identify the sound event as a noise event based on a correlation between the noise model and a signal envelope of the sound event; and where the processor is configured to model individual sound events that make up the noise of the noise model, and model a temporal space between the individual sound events. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A noise detection method, comprising:
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accessing, by a processor, a computer memory that stores a noise model that includes spectral and temporal shape characteristics of a noise; analyzing a signal to determine whether characteristics of the signal correspond to characteristics of the noise model; fitting, by the processor, the noise model to the signal in a time-frequency domain to evaluate spectral and temporal shape characteristics of a sound event in the signal; identifying, by the processor, the sound event as a noise event based on a correlation between the noise model and a signal envelope of the sound event; and modeling individual sound events that make up the noise of the noise model; and modeling a temporal space between the individual sound events. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A non-transitory computer-readable medium with instructions stored thereon, where the instructions are executable by a processor to cause the processor to perform the steps of:
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accessing a noise model that includes spectral and temporal shape characteristics of a noise; analyzing a signal to determine whether characteristics of the signal correspond to characteristics of the noise model; fitting the noise model to the signal in a time-frequency domain to evaluate spectral and temporal shape characteristics of a sound event in the signal; identifying the sound event as a noise event based on a correlation between the noise model and a signal envelope of the sound event; modeling individual sound events that make up the noise of the noise model; and modeling a temporal space between the individual sound events. - View Dependent Claims (14, 15, 16, 17)
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Specification