I want to get hexadecimal data from the filtered sound wave file. I have recorded voice and done noise filtering. Also the sound wave is converted to text file.Kindly help me

Here the entire process of code that i have done. Now i need to get hexadecimal data for the filtered sound signal that is plotted.

Fs=1000;
ch=1;
datatype='uint8';
nbits=16;
Nseconds=1;
ID=-1;

recorder=audiorecorder(Fs,nbits,ch,ID);
disp("Start Recording...");
recordblocking(recorder,Nseconds);
disp("End of recording...");
x=getaudiodata(recorder,datatype);
audiowrite('test.wav',x,Fs);
%plot graph of sound wave file
plot(x) 
%save as text file
[data, fs] = audioread('test.wav');
save test.txt data -ASCII



%noise reduction

%%1)   Load the 'audio_sample.wav' file in MATLAB
[sample_data, sample_rate] = audioread('test.wav');
% a.  Plot the signal in time and the amplitude of its frequency 
% components using the FFT.
sample_period = 1/sample_rate;
t = (0:sample_period:(length(sample_data)-1)/sample_rate);
subplot(2,2,1)
plot(t,sample_data)
title('Time Domain - Unfiltered Sound')
xlabel('Time (seconds)')
ylabel('Amplitude')
xlim([0 t(end)])
m = length(sample_data); % Original sample length.
n = pow2(nextpow2(m)); % Transforming the length so that the number of 
% samples is a power of 2. This can make the transform computation 
% significantly faster,particularly for sample sizes with large prime 
% factors.
y = fft(sample_data, n);
f = (0:n-1)*(sample_rate/n);
amplitude = abs(y)/n;
subplot(2,2,2)
plot(f(1:floor(n/2)),amplitude(1:floor(n/2)))
title('Frequency Domain - Unfiltered Sound')
xlabel('Frequency')
ylabel('Amplitude')
% b.  Listen to the audio file.
% sound(sample_data, sample_rate)
%%2)  Filter the audio sample data to remove noise from the signal.
Fs = sample_rate;                                       % Sampling Frequency (Hz)
Fn = Fs/2;                                              % Nyquist Frequency (Hz)
Wp = 40/Fn;                                           % Passband Frequency (Normalised)
Ws = 150/Fn;                                           % Stopband Frequency (Normalised)
Rp =   3;                                               % Passband Ripple (dB)
Rs = 60;                                               % Stopband Ripple (dB)
[n,Ws] = cheb2ord(Wp,Ws,Rp,Rs);                         % Filter Order
[z,p,k] = cheby2(n,Rs,Ws,'low');                        % Filter Design
[soslp,glp] = zp2sos(z,p,k);                            % Convert To Second-Order-Section For Stability
figure(3)
freqz(soslp, 2^16, Fs)                                  % Filter Bode Plot
filtered_sound = filtfilt(soslp, glp, sample_data);
sound(filtered_sound, sample_rate)


t1 = (0:sample_period:(length(filtered_sound)-1)/sample_rate);
subplot(2,2,3)
plot(t1,filtered_sound)
title('Time Domain - Filtered Sound')
xlabel('Time (seconds)')
ylabel('Amplitude')
xlim([0 t1(end)])
m1 = length(sample_data); % Original sample length.
n1 = pow2(nextpow2(m1)); % Transforming the length so that the number of 
% samples is a power of 2. This can make the transform computation 
% significantly faster,particularly for sample sizes with large prime 
% factors. 
y1 = fft(filtered_sound, n1);
f = (0:n1-1)*(sample_rate/n1);
amplitude = abs(y1)/n1;
subplot(2,2,4)
plot(f(1:floor(n1/2)),amplitude(1:floor(n1/2)))
title('Frequency Domain - Filtered Sound')
xlabel('Frequency')
ylabel('Amplitude')

[Walter Roberson]

It has been several decades since I last encountered a sound file that was stored as hexadecimal. Long before .wav files were invented. Around 1983.

It is very very unlikely that you want to read hexadecimal data from a sound file.