1. A method of transmitting an input audio signal, the method comprising:
quantizing a current spectral magnitude of the input audio signal;
feeding back a quantization error of a previous spectral magnitude to influence quantization of the current spectral magnitude, wherein feeding back comprises adaptively modifying a quantization criterion to form a modified quantization criterion;
minimizing a current quantization error by using the modified quantization criterion;
forming a quantized spectral envelope based on the minimizing; and
transmitting the quantized spectral envelope.
2. The method of claim 1, wherein minimizing further comprises using a noise-feedback solution.
3. The method of claim 1, wherein quantizing the spectral magnitudes comprises performing a scalar quantization.
4. The method of claim 3, wherein the scalar quantization comprises a direct scalar quantization.
5. The method of claim 3, wherein the scalar quantization comprises an indirect scalar quantization.
6. The method of claim 5, wherein:
the indirect scalar quantization comprises differential coding or Huffman coding; and
the quantization is performed in a log domain or a linear domain.
7. The method of claim 1, further comprising:
setting an initial quantization error of the current spectral magnitude to be Er(i)=Mq2(i)\u2212M(i), where M(i) is a current reference magnitude and Mq2(i) is a current quantized magnitude; and
setting an initial quantization error of a previous magnitude as Er(i\u22121)=Mq2(i\u22121)\u2212M(i\u22121), where M(i\u22121) is a previous reference magnitude and Mq2(i\u22121) is a previous quantized magnitude.
8. The method of claim 7, further comprising setting the current reference magnitude to be M(i)=maxVal\u2212log Gains(i), where maxVal is a maximum spectral magnitude and log Gains(i) is a spectral magnitude in a log domain.
9. The method of claim 7, wherein quantizing the current spectral magnitude comprises setting Mq2(i)=Index(i)\xb7Step, where Index(i) is a quantization index for each magnitude and Step is defined as Step=maxVal4 , where if Step>1.2, Step=1.2, and maxVal is a maximum spectral magnitude.
10. The method of claim 1, wherein minimizing the first quantization error comprises minimizing the expression MIN{|Mq2(0)\u2212M(0)|}, where M(0) is a first reference magnitude and Mq2(0) is said first quantized magnitude.
11. The method of claim 1, wherein minimizing the current quantization error comprises minimizing the expression MIN{|Mq2(i)\u2212M(i)\u2212\u03b1\u2003Er(i\u22121)|}, where M(i) is a current reference magnitude, Mq2(i) is said current quantized magnitude, Er(i\u22121) is a quantization error of a previous magnitude, and a is a constant (0<\u03b1<1) to control how much error noise is fed back from the quantization error Er(i\u22121) of the previous spectral magnitude.
12. The method of claim 11, wherein an overall energy of the quantized spectral envelope is not adjusted or normalized if \u03b1<=0.5.
13. The method of claim 11, wherein a is about 0.5.
14. The method of claim 1, further comprising normalizing an average magnitude of a quantized spectral envelope of the input audio signal in a time domain or a frequency domain.
15. The method of claim 1, further comprising:
receiving the quantized spectral envelope; and
forming an output audio signal based on the quantized spectral envelope.
16. The method of claim 15, further comprising driving a loudspeaker with the output audio signal.
17. The method of claim 1, wherein transmitting comprises transmitting over a voice over internet protocol (VOIP) network.
18. The method of claim 1, wherein transmitting comprises transmitting over a cellular telephone network.
19. A system for transmitting an input audio signal, the system comprising:
a transmitter comprising an audio coder, the audio coder configured to quantize a current spectral magnitude of the input audio signal;
feed back a quantization error of a previous spectral magnitude to influence quantization of the current spectral magnitude, wherein feeding back comprises adaptively modifying a quantization criterion to form a modified quantization criterion;
minimize a current quantization error by using the modified quantization criterion; and
form a quantized spectral envelope based on minimizing the current quantization error.
20. The system of claim 19, wherein the system is configured to operate over a voice over internet protocol (VOIP) system.
21. The system of claim 19, wherein the system is configured to operate over a cellular telephone network.
22. The system of claim 19, further comprising a receiver, the receiver comprising an audio decoder configured to receive the quantized spectral envelope and produce an output audio signal based on the quantized spectral envelope.
The claims below are in addition to those above.
All refrences to claims which appear below refer to the numbering after this setence.
1. A method, comprising:
accessing, by a device including a processor, a plurality of unauthenticated unique identification records associated with transactions between at least one client device and at least one server device during a specified time frame, wherein respective unauthenticated unique identification records are associated with respective unauthenticated unique identifiers of a plurality of unauthenticated unique identifiers;
selecting, by the device, a subset of the plurality of unauthenticated unique identification records that meet a selection criteria;
segmenting, by the device, the time frame into a plurality of disjoint time intervals;
determining, by the device, possible combinations of bit patterns representing the respective unauthenticated unique identifiers, wherein a length of the bit patterns equals a quantity of the time intervals and each bit of a bit pattern indicates whether a corresponding unauthenticated unique identifier has an associated unauthenticated unique identification record that meets the selection criteria for a time interval associated with the bit;
determining, by the device, a total quantity of possible churn patterns for the bit patterns;
determining, by the device, a total quantity of expected unauthenticated unique identifiers for all combinations of the bit patterns and the churn patterns; and
determining, by the device, a ratio of unauthenticated unique identifiers to unique users based upon the total quantity of expected unauthenticated unique identifiers and the total quantity of the churn patterns.
2. The method of claim 1, further comprising employing, by the device, an optimization algorithm for estimating respective best fit values for a set of parameters of a distribution function according to an optimization criteria, the set of parameters comprising respective capture probabilities for the time intervals indicating probability of an unauthenticated unique identifier having an associated unauthenticated unique identification record that meets the selection criteria during the time interval, a churn probability indicating the probability that the unauthenticated unique identifier is churned in the time intervals, and a rate of churn.
3. The method of claim 2, wherein the determining the total quantity of expected unauthenticated unique identifiers comprises:
determining respective probabilities of the churn patterns using the best fit set of estimated parameters;
determining respective first quantities of unauthenticated unique identifiers for each churn pattern and bit pattern combination given a rate of churns equaling one; and
determining respective second quantities of unauthenticated unique identifiers for each churn pattern and bit pattern combination pattern given a rate of churns greater than one.
4. The method of claim 3, wherein the determining the second quantity comprises determining the second quantity according to the distribution function.
5. The method of claim 2, further comprising proposing, by the device, one or more sets of starting values for the parameters.
6. The method of claim 5, further comprising determining, by the device, respective sets of estimated values for the parameters from the sets of starting values using the optimization algorithm.
7. The method of claim 6, further comprising selecting, by the device, the best fit set of estimated values from the sets of estimated values according to the optimization criteria.
8. The method of claim 1, wherein the optimization criteria is one of a maximum likelihood criteria, a least squares criteria, a mean squared error criteria, a least absolute deviations criteria, or an Lp spaces criteria.
9. The method of claim 1, wherein the distribution function is one of a Poisson distribution, a binomial distribution, a negative binomial distribution, a Bernoulli distribution, a geometric distribution, or a discrete uniform distribution.
10. The method of claim 1, further comprising:
determining, by the device, a quantity of the unauthenticated unique identifiers associated with the subset of unauthenticated unique identification records; and
determining, by the device, a number of unique users associated with the subset based upon the quantity of the unauthenticated unique identifiers and the ratio.
11. A non-transitory computer-readable medium having instructions stored thereon that, in response to execution, cause a system including a processor to perform operations comprising:
accessing a plurality of unauthenticated unique identification records associated with transactions between at least one client device and at least one server device during a specified time frame, wherein respective unauthenticated unique identification records are associated with respective unauthenticated unique identifiers of a plurality of unauthenticated unique identifiers;
selecting a subset of the plurality of unauthenticated unique identification records that meet a selection criteria;
segmenting the time frame into a plurality of disjoint time intervals;
determining possible combinations of bit patterns representing the respective unauthenticated unique identifiers, wherein a length of the bit patterns equals a quantity of the time intervals and each bit of a bit pattern indicates whether a corresponding unauthenticated unique identifier has an associated unauthenticated unique identification record that meets the selection criteria for a time interval associated with the bit;
determining a total quantity of possible churn patterns for the bit patterns;
determining a total quantity of expected unauthenticated unique identifiers for all combinations of the bit patterns and the churn patterns; and
determining a ratio of unauthenticated unique identifiers to unique users based upon the total quantity of expected unauthenticated unique identifiers and the total quantity of the churn patterns.
12. The non-transitory computer-readable medium of claim 11, further comprising employing an optimization algorithm for estimating respective best fit values for a set of parameters of a distribution function according to an optimization criteria, the set of parameters comprising respective capture probabilities for the time intervals indicating probability of an unauthenticated unique identifier having an associated unauthenticated unique identification record that meets the selection criteria during the time interval, a churn probability indicating the probability that the unauthenticated unique identifier is churned in the time intervals, and a rate of churn.
13. The non-transitory computer-readable medium of claim 12, wherein the determining the total quantity of expected unauthenticated unique identifiers comprises:
determining respective probabilities of the churn patterns using the best fit set of estimated parameters;
determining respective first quantities of unauthenticated unique identifiers for each churn pattern and bit pattern combination given a rate of churns equaling one; and
determining respective second quantities of unauthenticated unique identifiers for each churn pattern and bit pattern combination pattern given a rate of churns greater than one.
14. The non-transitory computer-readable medium of claim 13, wherein the determining the second quantity comprises determining the second quantity according to the distribution function.
15. The non-transitory computer-readable medium of claim 12, the operations further comprising proposing one or more sets of starting values for the parameters.
16. The non-transitory computer-readable medium of claim 15, the operations further comprising determining respective sets of estimated values for the parameters from the sets of starting values using the optimization algorithm.
17. The non-transitory computer-readable medium of claim 16, the operations further comprising selecting the best fit set of estimated values from the sets of estimated values according to the optimization criteria.
18. The non-transitory computer-readable medium of claim 11, the operations further comprising:
determining a quantity of the unauthenticated unique identifiers associated with the subset of unauthenticated unique identification records; and
determining a number of unique users associated with the subset based upon the quantity of the unauthenticated unique identifiers and the ratio.
19. A system comprising:
a processor; and
a memory communicatively coupled to the processor, the memory having stored therein computer-executable instructions, comprising:
a data processing component configured to:
access a plurality of unauthenticated unique identification records associated with transactions between at least one client device and at least one server device during a specified time frame, wherein respective unauthenticated unique identification records are associated with respective unauthenticated unique identifiers of a plurality of unauthenticated unique identifiers;
select a subset of the plurality of unauthenticated unique identification records that meet a selection criteria;
segment the time frame into a plurality of disjoint time intervals; and
determine possible combinations of bit patterns representing the respective unauthenticated unique identifiers, wherein a length of the bit patterns equals a quantity of the time intervals and each bit of a bit pattern indicates whether a corresponding unauthenticated unique identifier has an associated unauthenticated unique identification record that meets the selection criteria for a time interval associated with the bit; and
a modeling component configured to:
determine a total quantity of possible churn patterns for the bit patterns;
determine a total quantity of expected unauthenticated unique identifiers for all combinations of the bit patterns and the churn patterns; and
determine a ratio of unauthenticated unique identifiers to unique users based upon the total quantity of expected unauthenticated unique identifiers and the total quantity of the churn patterns.
20. The system of claim 19, wherein the modeling component is further configured to employ an optimization algorithm for estimating respective best fit values for a set of parameters of a distribution function according to an optimization criteria, the set of parameters comprising respective capture probabilities for the time intervals indicating probability of an unauthenticated unique identifier having an associated unauthenticated unique identification record that meets the selection criteria during the time interval, a churn probability indicating the probability that the unauthenticated unique identifier is churned in the time intervals, and a rate of churn.
21. The system of claim 20, wherein to determine the total quantity of expected unauthenticated unique identifiers comprises:
determine respective probabilities of the churn patterns using the best fit set of estimated parameters;
determine respective first quantities of unauthenticated unique identifiers for each churn pattern and bit pattern combination given a rate of churns equaling one; and
determine respective second quantities of unauthenticated unique identifiers for each churn pattern and bit pattern combination pattern given a rate of churns greater than one.
22. The system of claim 21, wherein to determine the second quantity comprises determine the second quantity according to the distribution function.
23. The system of claim 19, wherein the modeling component is further configured to propose one or more sets of starting values for the parameters.
24. The system of claim 23, wherein the modeling component is further configured to determine respective sets of estimated values for the parameters from the sets of starting values using the optimization algorithm.
25. The system of claim 24, wherein the modeling component is further configured to select the best fit set of estimated values from the sets of estimated values according to the optimization criteria.
26. The system of claim 19, wherein the modeling component is further configured to:
determine a quantity of the unauthenticated unique identifiers associated with the subset of unauthenticated unique identification records; and
determine a number of unique users associated with the subset based upon the quantity of the unauthenticated unique identifiers and the ratio.