Section A3 (Mars Mission: De-noising Speech)

This section should be implemented in MATLAB - mission.m. Be sure to include relevant figures and code snippets when presenting your results in your report. Senior analysts from the engineering team have determined that the received speech has been corrupted by an additive noise process. This model is illustrated in Figure 2.

Your primary objective in this task is to identify the noise signal and de-noise the speech (remove the noise). The speech is provided in the variable noiseSound. Original speech was recorded at a rate of 44100 samples per second for 20 seconds. Follow the instructions below to help you complete your task.

A3.1 One of the test signals that you have used above is the sample waveform of your periodic noise, which you will need to identify. Explain how you identified your noise waveform - consider things such as the period, offset and shape. (Criteria: 1c, 2b)

A3.2 Generate your noise waveform. Save this to the variable additive noise. It is to contain the same number of periods as the noise waveform in the corrupted speech signal. Make sure that an appropriate time domain vector, t, was generated for this waveform. (Criteria: 2b)

A3.3 Use MATLAB to evaluate the coefficients of your noise signal of either the Complex Fourier Series; c0 and cn for ?10 ? n ? 10, or the Trigonometric Fourier Series; a0, an and bn for 0 ? n ? 10. (Criteria: 2c)

A3.4 Write code to generate the Fourier series approximation (FS1), using the time vector t of your periodic noise. (Criteria: 2c

A3.5 Using FS1, recover the corrupted speech by reversing the additive process illustrated in Figure 2. Store the de-noised result in the variable dnSnd. (Criteria: 2a)

A3.6 Plot and listen to the recovered speech signal. Comment on visual changes as compared with the noisy speech signal, along with an explanation of what has happened. Include a transcription of the message, you just listened to, in your report. (Criteria: 1c, 2c)

MATLAB variables that should be included in your workspace for section A3 (mission.m), t - Time vector T - Period additive noise - Your noise waveform c0, cn - Complex Fourier series coefficient vectors, OR, a0, an, bn - Trig Fourier series coefficient vectors FS1 - Fourier series approximation vector dnSnd - De-noised resulting wave

MISSION.M FILE -

%% Assignment 1 Part A - Question 3
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%$%
% Do not change before line 28.
% If you have not generated Data1A from GenerateDataAssignment1A.m,
% do that now.

% Clearing and preparing the workspace
clear; clc; close all;

% Load assignment data.
load Data1A;

% VARIABLES:
% t - Time vector
% T - Period
% additive noise - Your noise waveform
% a0, an, bn - Trig Fourier series variables
% OR
% c0, cn - Complex Fourier series variables
% FS1 - Fourier series approximation
% dnSnd - De-noised resulting wave

%==================================================================
% Refer to the assignment sheet for details on variable naming.
% Names of the variables are important,
% e.g. 'a1' is considered a different variable to 'A1'.
%====Enter your code below this line================================

MATLAB GENERATED VARIABLES -

A = 2
B = 7
C = 1.25
noiseSound = 882000 x 1 double

Answer rating: 100% (QA)

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