is it possible to remove thermal noise completely from audiofile by using nlms or rls algorithm?

44 views (last 30 days)
% below is the code I came up with after many different references but some residue thermal noise still remains and % the filtered signal sounds grainy can it be made better or this is it
function adaptive_nlms_audio_filter()
% Adaptive NLMS Audio Noise Filtering with Dynamic Parameter Adjustment
clc;
clear;
close all;
% Get audio input from the user
[audio, fs] = getAudioInput();
% Normalize the audio
audio = audio / max(abs(audio));
% Add Nyquist-Johnson noise (white Gaussian noise)
noise_power = 0.01; % Adjust noise power as needed
noise = sqrt(noise_power) * randn(size(audio)); % Generate noise
noisy_audio = audio + noise;
% Analyze the spectrum of the input audio
[freq, magnitude] = analyzeSpectrum(noisy_audio, fs);
% Adaptive NLMS Parameters
M = determineFilterOrder(freq, magnitude); % Dynamically determine filter order
mu = determineStepSize(magnitude); % Dynamically determine step size
epsilon = 1e-6; % Regularization factor
w = zeros(M, 1); % Filter coefficients
filtered_nlms = zeros(size(noisy_audio));
% Adaptive filtering using NLMS
for i = M:length(noisy_audio)
x = flip(noisy_audio(i-M+1:i)); % Input vector
e = audio(i) - w' * x; % Error signal
w = w + (mu / (epsilon + norm(x)^2)) * x * e; % Update filter coefficients
filtered_nlms(i) = w' * x; % Filter output
end
% Plot the results
figure;
subplot(3, 1, 1);
plot(audio);
title('Original Audio');
xlabel('Samples');
ylabel('Amplitude');
subplot(3, 1, 2);
plot(noisy_audio);
title('Noisy Audio');
xlabel('Samples');
ylabel('Amplitude');
subplot(3, 1, 3);
plot(filtered_nlms);
title('Filtered Audio (Adaptive NLMS)');
xlabel('Samples');
ylabel('Amplitude');
% Buttons for playing audio
uicontrol('Style', 'pushbutton', 'String', 'Play Original', ...
'Position', [20 20 100 30], ...
'Callback', @(~,~) sound(audio, fs));
uicontrol('Style', 'pushbutton', 'String', 'Play Noisy', ...
'Position', [140 20 100 30], ...
'Callback', @(~,~) sound(noisy_audio, fs));
uicontrol('Style', 'pushbutton', 'String', 'Play Filtered', ...
'Position', [260 20 100 30], ...
'Callback', @(~,~) sound(filtered_nlms, fs));
end
function [audio, fs] = getAudioInput()
% Function to get audio input from user
choice = menu('Select Input Method', 'Record Audio', 'Load Audio File');
switch choice
case 1
% Record audio
fs = 44100; % Sampling rate
duration = 5; % Recording duration in seconds
recObj = audiorecorder(fs, 16, 1);
disp('Start speaking...');
recordblocking(recObj, duration);
disp('Recording stopped.');
audio = getaudiodata(recObj);
case 2
% Load audio file
[filename, pathname] = uigetfile({'*.wav', 'WAV Files (*.wav)'}, 'Select an Audio File');
if isequal(filename, 0)
error('No file selected.');
end
[audio, fs] = audioread(fullfile(pathname, filename));
otherwise
error('Invalid choice.');
end
% Normalize the audio
audio = audio / max(abs(audio));
end
function [freq, magnitude] = analyzeSpectrum(signal, fs)
% Analyze the frequency spectrum of the signal
N = length(signal);
freq = (0:N/2-1) * (fs / N);
fft_spectrum = fft(signal);
magnitude = abs(fft_spectrum(1:N/2));
end
function M = determineFilterOrder(freq, magnitude)
% Dynamically determine filter order based on signal spectrum
peak_freq = freq(magnitude == max(magnitude)); % Find the dominant frequency
if peak_freq < 1000
M = 32; % Low-frequency dominant signal
elseif peak_freq < 5000
M = 64; % Mid-frequency dominant signal
else
M = 128; % High-frequency dominant signal
end
end
function mu = determineStepSize(magnitude)
% Dynamically determine step size based on magnitude distribution
energy = sum(magnitude.^2);
if energy < 0.1
mu = 0.01; % Low-energy signal
elseif energy < 1
mu = 0.1; % Medium-energy signal
else
mu = 0.2; % High-energy signal
end
end
  1 Comment
Mathieu NOE
Mathieu NOE on 20 Nov 2024 at 17:02
hello
I am not sure these algorithms are the best choice for audio denoising (without some artifacts including original signal distorsion).
Have you looked at wavelet denoising ?

Sign in to comment.

Answers (0)

Categories

Find more on Audio I/O and Waveform Generation in Help Center and File Exchange

Products


Release

R2024b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!