How do I correctly implement IFFT in signal generation?

I'm trying to create a signal based on the frequency spectrum of a highly sampled noise signal muliplied by a gaussian window in the frequency domain centered at zero hz.

I'm multiplying the two together to create a combined frequency spectrum. Then I'm attempting to IFFT the combined spectrum to create a new signal.

Help would be greatly appreciated.

%% Highly Sampled Noise 

fsamp = 32e3; %Sampling frequency
Ts = 1/fsamp; %Period of sampling
L = 3.33; % Length of signal
t1 = 0:Ts:L-Ts;%vector 0:3.33 sampled at a rate of 32khz

Noise = 10*randn(1,length(t1));
plot(t1,Noise);
title('32kHz Noise Randn')
xlabel('Time seconds')
ylabel('Amplitude')
figure


%% Frequency Spectrum of Highly Sampled Noise
N = length(t1);
k = 0:N-1;
T = N/fsamp;
freq = k/T;
NFFT = 2^(nextpow2(N)+1);
N1f = abs(fft(Noise,NFFT)/N);
cutOff = ceil(N/2);
N1f = N1f(1:cutOff);
freq = freq(1:cutOff);
% freq = fsamp*linspace(0,1-(1/NFFT),NFFT);     % Frequency Vector
% mag_dB = 10*log10(N1f.^2);                    % Translating to Decibels
plot(freq,N1f)
title('Frequency Spectrum of the Noise');
xlabel('Frequency [Hz]');
ylabel('Normalized Magnitude |Noise(f)|');
grid on
figure

%% Gaussian Window using Custom Gauss Function Found Online
% CUSTOMGAUSS    Generate a custom 2D gaussian 
%
%    gauss = customgauss(gsize, sigmax, sigmay, theta, offset, factor, center)
%
%          gsize     Size of the output 'gauss', should be a 1x2 vector
%          sigmax    Std. dev. in the X direction
%          sigmay    Std. dev. in the Y direction
%          theta     Rotation in degrees
%          offset    Minimum value in output
%          factor    Related to maximum value of output, should be 
%                    different from zero
%          center    The center position of the gaussian, should be a
%                    1x2 vector   

% function ret = customgauss(gsize, sigmax, sigmay, theta, offset, factor, center)
% ret     = zeros(gsize);
% rbegin  = -round(gsize(1) / 2);
% cbegin  = -round(gsize(2) / 2);
% for r=1:gsize(1)
%     for c=1:gsize(2)
%         ret(r,c) = rotgauss(rbegin+r,cbegin+c, theta, sigmax, sigmay, offset, factor, center);
%     end
% end
% end 
% function val = rotgauss(x, y, theta, sigmax, sigmay, offset, factor, center)
% xc      = center(1);
% yc      = center(2);
% theta   = (theta/180)*pi;
% xm      = (x-xc)*cos(theta) - (y-yc)*sin(theta);
% ym      = (x-xc)*sin(theta) + (y-yc)*cos(theta);
% u       = (xm/sigmax)^2 + (ym/sigmay)^2;
% val     = offset + factor*exp(-u/2);
% end 


%Setting variables for CustomGauss
L2 = length(freq); 
stdX = length(1:Ts:1.1);
stdY = 1;
theta = 90;
Min_Val = 1;
Max_Val = 100;
Gauss_Win = customgauss([1,L2], stdX, stdY, theta, Min_Val, Max_Val, [0,-L2/2]);

plot(freq(1:L2),Gauss_Win(1:L2))
xlabel('Frequency [Hz]')
ylabel('Magnitude |W(f)| [dB]')
title('Gaussian Window in Frequency Domain')
figure

%% Tissue Spectrum: Gaussian Window and Noise Frequency Spectrum Combination
Tissue_Spec = N1f.*Gauss_Win; 
stem(freq,Tissue_Spec)
title('Window & Noise Combined Frequency')
xlabel('Frequency [Hz]')
ylabel('Magnitude |T(f)|') 
figure

%% IFFT and Highly Sampled Tissue Signal Creation
Tissue_Signal = ifft(Tissue_Spec,(NFFT))*L2;  
LN_TS = length(Tissue_Signal);
t2 = (0:(1-Ts)/(LN_TS-1):1-Ts);
plot(t2,real(Tissue_Signal))
title('Highly Sampled Tissue Signal')
xlabel('Time Seconds')
ylabel('Amplitude')
figure