Telecommunications speech signal improvement by reduction of residual noise

Claim 2

In a telecommunications network carrying incoming signals having both speech and noise energy, a method including iterative estimations using an LPC speech model for processing said signals at a selected point in said network to reduce said noise energy, comprising the steps of: converting said incoming signals to a time-series of spectral energy data frames; reducing in an initial stage comprising said LPC speech model the noise energy in each frame, thereby creating noise-reduced data frames having residual low- amplitude non-stationary noise appearing randomly in data frames as false formant pickets or other noise components of varying amplitudes; selecting pickets in each said data frame which in accordance with a first criterion are likely to represent false formant pickets; minimizing the variation in amplitudes of said selected pickets with the pickets in corresponding positions in adjacent said data frames, thereby creating noise-minimized frames; and combining said noise-minimized frames with said noise-reduced frames and transmitting the combined signal through said network.

Claim 3

The method of claim 1, comprising the further step of: minimizing the within-frame variations in amplitude of further said pickets identified in accordance with a second predetermined criterion as likely to represent said other noise components.

Claim 5

In a telecommunications network carrying incoming signals having both speech and noise energy, a method including iterative estimations using an LPC speech model for processing said signals at a selected point in said network to reduce said noise energy, comprising the steps of: converting said incoming signals to a time-series of spectral energy data frames; reducing in an initial stage comprising said LPC speech model the noise energy in each said frame, thereby creating noise-reduced data frames having residual low-amplitude non-stationary noise appearing randomly in data frames as false formant pickets or other noise components of varying amplitudes; making an estimate of the noise power spectrum for each said data frame; identifying in successive said data frames any said pickets at or below a threshold amplitude set at a defined distance above said noise power spectrum estimate for each said data frame. comparing the amplitude of each identified picket in a given data frame to the amplitudes of corresponding said pickets in time-adjacent data frames to determine which has the minimum amplitude; combining said noise-minimized frames with said noise-reduced frames; and substituting said minimum value for the amplitude of each said identified picket; and transmitting the combined signal through said network.

Claim 6

In a telecommunications network carrying incoming signals having both speech and noise energy, a method including iterative estimations using an LPC speech model for processing said signals at a selected point in said network to reduce said noise energy, comprising the steps of: converting said incoming signals to a time-series of spectral energy data frames; reducing in an initial stage comprising said LPC speech model the noise energy in each said frame, thereby creating noise-reduced data frames having residual low-amplitude non-stationary noise appearing randomly in data frames as false formant pickets or other noise components of varying amplitudes; making an estimate of the noise power spectrum for each said data frame; identifying in successive said data frames any said pickets at or below a threshold amplitude set at a defined distance above said noise power spectrum estimate for each said data frame. comparing the amplitude of each identified picket in a given data frame to the amplitudes of corresponding said pickets in time-adjacent data frames to determine which has the minimum amplitude; combining said noise-minimized frames with said noise-reduced frames; and substituting said minimum value for the amplitude of each said identified picket; and transmitting the combined signal through said network.

Claim 7

The method of claim 4, wherein said successive time-adjacent frames are the frames immediately adjacent to said given frame.

Claim 8

The method of claim 5, wherein said defined distance according to claim 4 is not substantially greater than one power spectrum standard deviation above said noise power spectrum estimate established for each said frame.

Claim 10

The method of claim 7, wherein said nearby pickets are the pair located two picket positions on either side of said identified picket.

Claim 11

The method of claim 8, wherein said downward adjustment step comprises: averaging the values of said pair of pickets; and substituting the result for the amplitude values of said identified picket and of the pickets next-adjacent to said identified picket.

Claim 13

The method of claim 10, wherein said said noise power spectrum estimate is generated in said initial stage.

Claim 14

The method of claim 10, wherein said predetermined fraction is in a range of up to 50 percent of said noise power spectrum estimate.

Claim 15

The method of claim 9, comprising the further steps of: spectrally subtracting from said converted signal a predetermined fraction of said noise power spectrum estimate; and applying the resultant said spectrally reduced signal to said initial stage.

Claim 16

The method of claim 13, wherein said noise power spectrum estimate is generated in said first stage.

Claim 17

The method of claim 14, wherein said predetermined fraction is in a range of up to 50 percent of said noise power spectrum estimate.

Claim 18

The method of claim 3, wherein said selected point in said network is a toll switch.

Claim 19

The method of claim 7, wherein said selected point in said network is a toll switch.

Claim 20

The method of claim 10, wherein said selected point in said network is a toll switch.

Claim 21

The method of claim 15, wherein said selected point in said network is a toll switch.

Claim 22

In a telecommunications network carrying incoming signals having both speech and noise energy, a method for processing said signals at a selected point in said network to reduce said noise energy, comprising the steps of: converting said incoming signals to a time-series of special energy data frames; using an iterated filter based on an LPC speech modal with filter-order adjustments, iteratively processing said data frames to create a new filter for each iteration of a current frame, a noise power spectra estimate for each frame and a noise-reduced output speech signal for each iteration, thereby also creating residual low-amplitude non-stationary noise appearing randomly in data frame pickets at false formant or other noise components; detecting noise-only frames and in response thereto updating said noise power spectrum estimate only during said noise-only frames; identifying in successive said data frames from the output speech signal of said iterative filter any said pickets at or below a threshold amplitude set at a defined distance above said noise power spectrum estimate for each said data frame; comparing the amplitude of each identified picket in a given data frame to the amplitudes of corresponding said pickets in time-adjacent data frames to determine which has the minimum amplitude; substituting said minimum value for the amplitude of each said identified picket; combining said noise-minimized frames with said noise-reduced frames; and transmitting the combined signal through said network.

Claim 23

In a telecommunications network carrying incoming signals having both speech and noise energy, a method for processing said signals at a selected point in said network to reduce said noise energy, comprising the steps of: converting said incoming signals to a time-series of special energy data frames; using an iterated filter based on an LPC speech modal with filter-order adjustments, iteratively processing said data frames to create a new filter for each iteration of a current frame, a noise power spectra estimate for each frame and a noise-reduced output speech signal for each iteration, thereby also creating residual low-amplitude non-stationary noise appearing randomly in data frame pickets at false formant or other noise components; detecting noise-only frames and in response thereto updating said noise power spectrum estimate only during said noise-only frames; identifying in successive said data frames from the output speech signal of said iterative filter any said pickets at or below a threshold amplitude set at a defined distance above said noise power spectrum estimate for each said data frame; comparing the amplitude of each identified picket in a given data frame to the amplitudes of corresponding said pickets in time-adjacent data frames to determine which has the minimum amplitude; substituting said minimum value for the amplitude of each said identified picket; combining said noise-minimized frames with said noise-reduced frames; and transmitting the combined signal through said network.

Claim 24

The method of claim 20, comprising the further step of: attenuating said incoming signal in further response to said detection of noise-only frames; and passing said attenuated incoming signal to said network.

Claim 25

The method of claim 21, comprising the further step of: identifying in successive said data frames any said pickets at or below a threshold amplitude set at a defined distance above said noise power spectrum estimate for each said data frame; comparing the amplitude of each identified picket in a given data frame to the amplitude of corresponding said pickets in time-adjacent data frames to determine which has the minimum amplitude; substituting said minimum value for the amplitude of each said identified picket to further minimize noise components therein; combining said noise-minimized frames with said noise-reduced frames; and transmitting the combined signal to said network.

Claim 26

The method of claim 22, comprising the further step of: detecting voiced speech and in response thereto adjusting said LPC modal filter order to substantially the 10 th order.

Claim 27

The method of claim 23, comprising the further step of detecting unvoiced speech and in response thereto adjusting the LPC modal filter order to a range of from 4 th to 6 th order.

Claim 28

The method of claim 24, wherein said successive time-adjacent frames are the frames immediately adjacent to said given frame.

Claim 29

The method of claim 25, wherein said iterative filter operations further comprise: selecting from said time-series of data frames a short sequence of consecutive frames including the current frame; iterating the current frame over only said frame sequence; and performing iterations numbering from three to seven on said current frame, thereby to create a smoothed estimate of the speech power spectrum for said frame.

Claim 30

The method of claim 26, wherein said iterative processing further comprises creating a new Wiener Filter for each iteration of a current frame by combining the output of the previous Wiener Filter iteration with the unfiltered said incoming signal in a preselected weighting ratio, thereby to control through said weighting the final noise content of said output signal vs. the degree of high-frequency filtering in said iterative filtering.

Claim 31

The method of claim 27, wherein said short sequence of frames includes from 1 to 2 future frames and from 1 to 4 past frames.

Claim 32

The method of claim 28, wherein said data frames during said iterations are overlapped.

Claim 33

The method of claims 20, 21, 22, 23, 24, 25, 26, 27, 28, or 29, wherein said defined distance according to claim 20 is not substantially greater than one power spectrum standard deviation above said noise power spectrum estimate for each said frame.

Claim 34

The method of claim 30, comprising the further steps of: identifying in successive ones of said data frames one or more spectral pickets having amplitudes within a range which according to a predetermined criterion are likely to represent non-stationary noise energy; comparing within the same frame the amplitude of each said identified spectral picket with the amplitudes of nearby pickets of different frequency values to determine if any of said nearby pickets have amplitudes falling below said range; and if any are found, adjusting downward the amplitude of each identified said picket; and transmitting the frames with downward-adjusted picket amplitudes through said network.

Claim 35

The method of claim 31, wherein said nearby pickets are the pair located two picket positions on either said of said identified picket.

Claim 36

The method of claim 32, wherein said downward adjustment step comprises: averaging the values of said pair of pickets; and substituting said average for the amplitude values of said identified picket and of the next-adjacent to said identified picket.

Claim 38

The method of claim 34, wherein said predetermined fraction is in a range of up to 50 percent of said noise power spectrum estimate.

Claim 39

The method of claim 35, wherein said data frames substantially overlap one another by substantially 50 percent.

Claim 41

The method of claim 30, wherein said selected point in said network is a toll switch.