We claim:
1. A method of preventing adhesion formation between tissues in an animal comprising placing a sterile adhesion prevention barrier between the tissues of the animal where the adhesion to be prevented wherein the sterile adhesion prevention barrier is formed from a polyoxaester having a first divalent repeating unit of formula IA:
OC(O)C(R1)(R2)OR3OC(R1)(R2)C(O)IA
and a second repeating unit selected from the group of formulas consisting of:
OR4A,II OR5C(O)B,III OR5C(O)PO)LGXI
and combinations thereof wherein R1, R1, R2 and R2 are independently hydrogen or an alkyl group containing 1 to 8 carbon atoms; R3 is an alkylene unit containing from 2 to 12 carbon atoms or is an oxyalkylene group of the following formula:
(CH2)COD(CH2)IV
wherein C is an integer in the range of from 2 to about 5, D is an integer in the range of from about 0 to about 2,000, and E is an integer in the range of from about 2 to about 5, except when D is zero, in which case E will be an integer from 2 to 12; R4 is an alkylene unit containing from 2 to 8 carbon atoms ; A is an integer in the range of from 1 to 2,000; R5 is selected from the group consisting of C(R6)(R7), (CH2)3O, CH2CH2OCH2, CR8HCH2, (CH2)5, (CH2)FOC(O) and (CH2)FC(O)CH2; R6 and R7 are independently hydrogen or an alkyl containing from 1 to 8 carbon atoms; R8 is hydrogen or methyl; F is an integer in the range of from 2 to 6; B is an integer in the range of from 1 to n such that the number average molecular weight of formula III is less than about 200,000; P is an integer in the range of from 1 to m such that the number average molecular weight of formula XI is less than about 1,000,000; G represents the residue minus from 1 to L hydrogen atoms from the hydroxyl groups of an alcohol previously containing from 1 to about 200 hydroxyl groups; and L is an integer from about 1 to about 200.
2. The method of claim 1 wherein additionally present is a third divalent repeating unit of the formula:
OC(O)R30C(O)IB
wherein R30 is an alkylene, arylene, arylalkylene, substituted alkylene, substituted arylene and substituted alkylarylene provided that R30 cannot be C(R1)(R2)1 2O(R3)OC (R1)(R2)1-2.
3. The method of claim 1 wherein the number average molecular weight of formula III contained in the polyoxaester is less than 100,000.
4. The method of claim 1 wherein the aliphatic polyoxaester has the following repeating units:
OC(O)C(R1)(R2)O(R3)OC(R1)(R2)C(O) and (OR4)A.
5. The method of claim 1 wherein the aliphatic polyoxaester has the following repeating units:
OC(O)C(R1) (R2)OR3OC(R1) (R2)C(O);
OR4A; and
OR5C(O)B.
6. The method of claim 5 wherein R3 is an oxyalkylene group.
7. The method of claim 6 wherein the first repeating unit is derived from a dicarboxylic acid selected from the group consisting of 3,6-dioxaoctanedioic acid, 3,6,9-trioxaundecanedioic acid and combinations thereof.
8. The method of claim 5 wherein the second repeating unit is derived from a diol selected from the group consisting of 1,2-ethandiol, 1,2-propandiol, 1,3-propandiol and combinations thereof.
9. The method of claim 5 wherein at least one of the second repeating unit is derived from ethylene glycol.
10. The method of claim 1 wherein at least one of the second repeating unit is derived from a lactone selected from the group consisting of glycolide, lactide, -caprolactone and combinations thereof.
11. The method of claim 7 wherein the polyoxaester has two second repeating units wherein one of the second repeating units is a diol selected from the group consisting of 1,2-ethandiol, 1,2-propandiol, 1,3-propandiol and combinations thereof and the other repeating unit is a lactone selected from the group consisting of glycolide, lactide, -caprolactone and combinations thereof.
12. The method of claim 1 wherein the aliphatic polyoxaester has the following repeating units:
OC(O)C(R1) (R2)OR3OC(R1) (R2)C(O) OR4A; and
(OR5C(O)PO)LG.
13. The method of claim 2 wherein the polyoxaester has the following repeating units:
OC(O)R30C(O),
OR4A,
OC(O)C(R1) (R2)OR3OC(R1) (R2)C(O),
OR4A, and
OR5C(O)
wherein R4 and R4 are independently selected from alkylene groups containing from 2 to 8 carbon atoms; A and A are independently integers in the range of from 1 to about 2,000.
14. The method of claim 2 wherein the polyoxaester copolymer has the formula:
OC(O)R30C(O)
OR4A,
OC(O)C(R1) (R2)OR3OC(R1) (R2)C(O)
OR4A
(OR5C(O)PO)LG
wherein R4 and R4 are independently selected from alkylene groups containing from 2 to 8 carbon atoms; A and A are independently integers in the range of from 1 to about 2,000.
15. The method of claim 1 wherein the polyoxaester copolymer is linked to one or more polymerizable regions.
16. The method of claim 1 wherein the polyoxaester copolymer has been crosslinked.
17. The method of claim 16 wherein the polyoxaester copolymer has been crosslinked by the addition of a coupling agent.
18. The method of claim 16 wherein the crosslinked polyoxaester copolymer has been contacted with water to form a hydrogel.
19. The method of claim 2 wherein the barrier is a film.
20. The method of claim 2 wherein the barrier is a foam.
21. The method of claim 2 wherein the barrier is a felt.
22. The method of claim 2 wherein the barrier is a gel.
23. The method of claim 2 wherein the barrier is a liquid.
24. The method of claim 1 wherein the polyoxaester is blended with a second polymer selected from the group consisting of homopolymer and copolymer of lactone type polymers with the repeating units described by formulas III and XI, aliphatic polyurethanes, polyether polyurethanes, polyester polyurethanes, polyethylene copolymers, polyamides, polyvinyl alcohols, poly(ethylene oxide), polypropylene oxide, polyethylene glycol, polypropylene glycol, polytetramethylene oxide, polyvinyl pyrrolidone, polyacrylamide, poly(hydroxy ethyl acrylate), poly(hydroxyethyl methacrylate), absorbable polyoxalates, absorbable polyanhydrides and combinations thereof.
25. A aliphatic polyoxaester having a first repeating unit of the formula:
OC(O)C(R1)(R2)OR3OC(R1)(R2)C(O)
and a second repeating units are
OR4A; and
OR5C(O)B.
wherein R1, R1,, R2 and R2 are independently hydrogen or an alkyl group containing 1 to 8 carbon atoms; R3 is an alkylene unit containing from 2 to 12 carbon atoms or is an oxyalkylene group of the following formula:
(CH2)COD(CH2)EIV
wherein C is an integer in the range of from 2 to about 5, D is an integer in the range of from about 0 to about 2,000, and E is an integer in the range of from about 2 to about 5, except when D is zero, in which case E will be an integer from 2 to 12; R4 is an alkylene unit containing from 2 to 8 carbon atoms ; A is an integer in the range of from 1 to 2,000; R5 is selected from the group consisting of C(R6) (R7), (CH2)3O, CH2CH2OCH2, CR8HCH2, (CH2)5, (CH2)FOC(O) and (CH2)FC(O)CH2; R6 and R7 are independently hydrogen or an alkyl containing from 1 to 8 carbon atoms; R8 is hydrogen or methyl; F is an integer in the range of from 2 to 6; B is an integer in the range of from 1 to n such that the number average molecular weight of formula III is less than about 200,000.
26. The aliphatic polyoxaester of claim 25 wherein R3 is an oxyalkylene group.
27. The aliphatic polyoxaester of claim 26 wherein the first repeating unit is derived from a dicarboxylic acid selected from the group consisting of 3,6-dioxaoctanedioic acid, 3,6,9-trioxaundecanedioic acid and combinations thereof.
28. The aliphatic polyoxaester of claim 25 wherein the second repeating unit is derived from a diol selected from the group consisting of 1,2-ethandiol, 1,2-propandiol, 1,3-propandiol and combinations thereof.
29. The aliphatic polyoxaester of claim 25 wherein the second repeating unit is derived from ethylene glycol.
30. The aliphatic polyoxaester of claim 25 wherein the second repeating unit is derived from a lactone selected from the group consisting of glycolide, lactide, -caprolactone and combinations thereof.
31. The aliphatic polyoxaester of claim 27 wherein the aliphatic polyoxaester has two second repeating units wherein one of the second repeating units is a diol selected from the group consisting of 1,2-ethandiol, 1,2-propandiol, 1,3-propandiol and combinations thereof and the other repeating unit is a lactone selected from the group consisting of glycolide, lactide, -caprolactone and combinations thereof.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
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.
2. The method of claim 1 further comprising:
performing the steps of obtaining a static-based prediction, obtaining a dynamic-based prediction, determining a measure of the probability of the input feature vector occurring, combining the static-based prediction and the dynamic-based prediction, and multiplying the combined prediction by the measure of the probability for each of a set of mixture components, each mixture component providing a component of the reduced-noise feature vector; and
combining each component of the reduced-noise feature vector together to form the reduced-noise feature vector.
3. The method of claim 1 wherein obtaining a static-based prediction comprises using a mean of a distribution of the prior model of static aspects of clean signals as the static-based prediction.
4. The method of claim 1 wherein obtaining a dynamic-based prediction comprises adding a noise-reduced feature vector from a preceding frame to a mean of a distribution of the prior model of dynamic aspects of clean signals.
5. The method of claim 1 wherein combining the static-based prediction and the dynamic-based prediction comprises:
multiplying the static-based prediction by a static-specific weighting value to produce a weighted static-based prediction;
multiplying the dynamic-based prediction by a dynamic-specific weighting value to produce a weighted dynamic-based prediction;
adding the weighted static-based prediction to the weighted dynamic-based prediction to form at least part of the combined prediction.
6. The method of claim 5 wherein the static-specific weighting value is a function of a variance of a distribution in the prior model of dynamic aspects of clean signals.
7. The method of claim 5 wherein the dynamic-specific weighting value is a function of a variance of a distribution in the prior model of static aspects of clean signals.
8. The method of claim 1 further comprising obtaining an acoustic environment-based prediction of the noise-reduced feature vector and wherein combining the static-based prediction and the dynamic-based prediction further comprises combining the static-based prediction, the dynamic-based prediction and the acoustic environment-based prediction to form at least part of the combined prediction.
9. The method of claim 8 wherein obtaining an acoustic environment-based prediction comprises:
estimating noise in a frame of the noisy input signal; and
using the noise estimate in a function to determine the acoustic environment-based prediction.
10. The method of claim 9 wherein using the noise estimate in a function comprises using the noise estimate in a function that includes a Taylor Series approximation.
11. The method of claim 10 wherein the Taylor series approximation is a zero-th order Taylor series expansion.
12. The method of claim 11 wherein using the noise estimate in a function further comprises also using the input feature vector in the function.
13. A computer-readable medium having computer-executable instructions for performing steps comprising:
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.
14. The computer-readable medium of claim 13 wherein using a prior model of static aspects of clean speech comprises selecting a mean of a distribution in the prior model of static aspects of clean speech as the static-based predicted value.
15. The computer-readable medium of claim 13 wherein using a prior model of dynamic aspects of clean speech comprises adding a noise-reduced feature vector to a mean of a distribution in the prior model of dynamic aspects of clean speech to produce the dynamic-based predicted value.
16. The computer-readable medium of claim 13 wherein the acoustic environment model is formed from a Taylor series approximation to the non-linear function.
17. The computer-readable medium of claim 13 wherein combining the static-based predicted value, the dynamic-based predicted value and the acoustic environment-based predicted value comprises:
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.
18. The computer-readable medium of claim 17 wherein the weight applied to the static-based predicted value is a function of a variance for a distribution in the prior model of the dynamic aspects of clean speech.
19. The computer-readable medium of claim 17 wherein the weight applied to the dynamic-based predicted value is a function of a variance for a distribution in the prior model of the static aspects of clean speech.
20. The computer-readable medium of claim 13 further comprising:
repeating the steps to produce a static-based predicted value, to produce a dynamic-based predicted value, to produce an acoustic environment-based predicted value, and to combine the static-based predicted value, the dynamic-based predicted value and the acoustic environment-based predicted value for each mixture component in a set of mixture components to thereby produce a set of components for the noise-reduced feature vector; and
combining the components in the set of components for the noise-reduced feature vector to produce the noise-reduced feature vector.
21. A computer-readable medium having computer-executable instructions for performing steps comprising:
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.
22. The computer-readable medium of claim 21 wherein using a prior model of static aspects of clean speech comprises selecting a mean of a distribution in the prior model of static aspects of clean speech as the static-based predicted value.
23. The computer-readable medium of claim 21 wherein using a prior model of dynamic aspects of clean speech comprises adding a noise-reduced feature vector to a mean of a distribution in the prior model of dynamic aspects of clean speech to produce the dynamic-based predicted value.
24. The computer-readable medium of claim 21 wherein applying a noisy feature vector to an acoustic environment model further comprises applying an estimate of the noise in the frame to the acoustic environment model.
25. The computer-readable medium of claim 24 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.
26. The computer-readable medium of claim 25 wherein the acoustic environment model is formed from a Taylor series approximation to the non-linear function.
27. The computer-readable medium of claim 21 wherein the weight applied to the static-based predicted value is a function of a variance for a distribution in the prior model of the dynamic aspects of clean speech.
28. The computer-readable medium of claim 21 wherein the weight applied to the dynamic-based predicted value is a function of a variance for a distribution in the prior model of the static aspects of clean speech.
29. The computer-readable medium of claim 21 further comprising:
repeating the steps to produce a static-based predicted value, to produce a dynamic-based predicted value, to produce an acoustic environment-based predicted value, and to combine the static-based predicted value, the dynamic-based predicted value and the acoustic environment-based predicted value for each mixture component in a set of mixture components to thereby produce a set of components for the noise-reduced feature vector; and
combining the components in the set of components for the noise-reduced feature vector to produce the noise-reduced feature vector.