Numpy Solve a linear prediction problem
Linear prediction is a mathematical operation where future values of a discrete-time signal are estimated as a linear function of previous samples. In digital signal processing, linear prediction is often called linear predictive coding (LPC). One of its applications is speech coding.
Data set for activity 2 is here. Use data set No. n in your test, where n = 1 + ((sum of all digits of your student numbers) mod 5). For example, if your student number is 19435207, then use data set 2 (because 1 + ((1+9+4+3+5+2+0+7) mod 5) = 2).
-0.9366009879004845
1.0824437995513136
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0.3205884531124265
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0.7681994537973332
1.63650862771123
1.7187954873089346
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0.3982049603972174
2.159331267452506
2.5137192341436156
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1.5575024336070087
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1.08384621638991
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-1.048325564385233
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Use the data sheet like this for the graph and the deadline is tomorrow so please make it asap
Homework Answers
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a.A source speech signal is encoded by an 8th-order Linear Predictive Coding (LPC) parametric model using...
a.A source speech signal is encoded by an 8th-order Linear Predictive Coding (LPC) parametric model using a frame analysis length of 20ms and with a 10% frame overlap. How many frames per second will be encoded by the LPC model? (2 marks) b.Following quantising in the LPC model described in part a., an average of 5 bits per frame is allocated to transmit each predictor coefficient. If it is assumed that 1 bit is allocated for the input excitation (U/V)...
It is decided to use an AR model (a linear prediction model) of an observed signal...
It is decided to use an AR model (a linear prediction model) of
an observed signal x(n) in order to estimate the
signal’s power spectral density. The model linear prediction
parameters will be determined using least squares
analysis, based on the equations:
1. In a particular experiment we observe x(n) = 1.0, 1.1, 1.2,
1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9 for n = 0, 1, 2, 3, 4,
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3. Problem 3 [Impulse). One of the most important digital signals is the so-called unit impulse...
3. Problem 3 [Impulse). One of the most important digital signals is the so-called unit impulse sequence, which is a discrete time function whose sample is equal to zero for all values of the time index n except n = 0, where it has unity value, that is, Ji, n=0, 8[n] = 0, n+0. Page 6 (a) (2 points) Draw a graphical representation of the signal 8[n]. The horizontal axis should indicate the time-index value n = ..., -2,-1,0,1,2,... and...
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Implement the following machine learning tasks, utilizing Regression techniques e Prediction e Classification eFeature Reduction Feature Independence Model Selection (underfitting and overfitting analysis) 2 Required Components You may utilize Python or Matlab libraries, for the following implementations. 1. Implement prediction utilizing multiple linear regression on a data set with several features Perform an evaluation of the residuals to check for assumptions of your model, such as li earity, noise term with zero mean and constant variance, normality and so forth....
Question 2 (a) Determine whether the discrete time system which has an output y[n] 2*x[n] over...
Question 2 (a) Determine whether the discrete time system which has an output y[n] 2*x[n] over the nterval 010 is linear or not by determining the response yi[n] to the input signalxj[n]- sin( (2*pi / 10 ) * n ) and the response y2[n] to the input signal x2[n] = cos( (2*pi/10 ) * n ). Determine the response y3[n] to the input signal x1n] = xi [n] + x2[n] and compare it with y4[n] = [n] + y2[n] ....
5. Confidence intervals for prediction You are a starting pitcher in the major leagues. It's January 2008, and you are in the process of negotiating your salary for the 2008 season. You hire a st...
5. Confidence intervals for prediction You are a starting pitcher in the major leagues. It's January 2008, and you are in the process of negotiating your salary for the 2008 season. You hire a statistician to help you with your negotiations. She specifies the following simple linear regression model: where y2008 salary (in millions of dollars) and x-performance during the regular 2007 season Then she selects a random sample of 50 major league starting pitchers with signed salary agreements for...
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matlab
matlab
For this problem you will test a few interpolation approaches for the application of generating interpolated data. We'll do this by interpolating data that is sampled from a known mathematical function. The same function can then be used to compute true values at the interpolated points to use in error Consider the following mathematical function (Runge's function): 1+25r2 Write a function mfile that uses this formula to generate a set of data use those points along approaches outlined...
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Linear Algebra. please complete or show me how to to
complete it. idk what to do. thank you
Curve Fitting It is desired to fit a polynomial curve through evenly spaced (x-direction) points. The general form of a polynomial is: f(x) = 4x + 4-1x* +ajx + ao If one wishes to fit a curve through, say 4 points, one would need a 3rd degree polynomial (n = 3) such that 4 unknown constants could be evaluated. In the absence...
4. We have the following data r 12 3 2 4.2 5. When you fitted a linear model to this data set, you solved a least squares problem. Your task here is to perform a SVD and then use it to solve the...
4. We have the following data r 12 3 2 4.2 5. When you fitted a linear model to this data set, you solved a least squares problem. Your task here is to perform a SVD and then use it to solve the least squares problem.
4. We have the following data r 12 3 2 4.2 5. When you fitted a linear model to this data set, you solved a least squares problem. Your task here is to perform...
Could you please answer the question Q1 to Q3. Write the answer clearly and step by step. 1 Let U = {1, 2, 3, 4, 5, 6, 7} be the universe. Form the set A as follows: Read off your seven digit student...
Could you please answer the question Q1 to Q3. Write the answer
clearly and step by step.
1 Let U = {1, 2, 3, 4, 5, 6, 7} be the universe. Form the set A as follows: Read off your seven digit student number from left to right. For the first digit ni include the number 1 in A if ni is even otherwise omit 1 from A. Now take the second digit n2 and include the number 2 in...
It is decided to use an AR model (a linear prediction model) of
an observed signal x(n) in order to estimate the
signal’s power spectral density. The model linear prediction
parameters will be determined using least squares
analysis, based on the equations:
1. In a particular experiment we observe x(n) = 1.0, 1.1, 1.2,
1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9 for n = 0, 1, 2, 3, 4,
5, 6, 7, 8, 9 respectively. Fill in specific numeric values...
3. Problem 3 [Impulse). One of the most important digital signals is the so-called unit impulse sequence, which is a discrete time function whose sample is equal to zero for all values of the time index n except n = 0, where it has unity value, that is, Ji, n=0, 8[n] = 0, n+0. Page 6 (a) (2 points) Draw a graphical representation of the signal 8[n]. The horizontal axis should indicate the time-index value n = ..., -2,-1,0,1,2,... and...
Implement the following machine learning tasks, utilizing Regression techniques e Prediction e Classification eFeature Reduction Feature Independence Model Selection (underfitting and overfitting analysis) 2 Required Components You may utilize Python or Matlab libraries, for the following implementations. 1. Implement prediction utilizing multiple linear regression on a data set with several features Perform an evaluation of the residuals to check for assumptions of your model, such as li earity, noise term with zero mean and constant variance, normality and so forth....
Question 2 (a) Determine whether the discrete time system which has an output y[n] 2*x[n] over the nterval 010 is linear or not by determining the response yi[n] to the input signalxj[n]- sin( (2*pi / 10 ) * n ) and the response y2[n] to the input signal x2[n] = cos( (2*pi/10 ) * n ). Determine the response y3[n] to the input signal x1n] = xi [n] + x2[n] and compare it with y4[n] = [n] + y2[n] ....
5. Confidence intervals for prediction You are a starting pitcher in the major leagues. It's January 2008, and you are in the process of negotiating your salary for the 2008 season. You hire a statistician to help you with your negotiations. She specifies the following simple linear regression model: where y2008 salary (in millions of dollars) and x-performance during the regular 2007 season Then she selects a random sample of 50 major league starting pitchers with signed salary agreements for...
matlab
matlab
For this problem you will test a few interpolation approaches for the application of generating interpolated data. We'll do this by interpolating data that is sampled from a known mathematical function. The same function can then be used to compute true values at the interpolated points to use in error Consider the following mathematical function (Runge's function): 1+25r2 Write a function mfile that uses this formula to generate a set of data use those points along approaches outlined...
Linear Algebra. please complete or show me how to to
complete it. idk what to do. thank you
Curve Fitting It is desired to fit a polynomial curve through evenly spaced (x-direction) points. The general form of a polynomial is: f(x) = 4x + 4-1x* +ajx + ao If one wishes to fit a curve through, say 4 points, one would need a 3rd degree polynomial (n = 3) such that 4 unknown constants could be evaluated. In the absence...
4. We have the following data r 12 3 2 4.2 5. When you fitted a linear model to this data set, you solved a least squares problem. Your task here is to perform a SVD and then use it to solve the least squares problem.
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Could you please answer the question Q1 to Q3. Write the answer
clearly and step by step.
1 Let U = {1, 2, 3, 4, 5, 6, 7} be the universe. Form the set A as follows: Read off your seven digit student number from left to right. For the first digit ni include the number 1 in A if ni is even otherwise omit 1 from A. Now take the second digit n2 and include the number 2 in...
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