GNU Octave:实际 FFT 数据的 1/N Octave 平滑(不是它的表示)
GNU Octave: 1/N Octave Smoothing of actual FFT Data (not the representation of it)
我想平滑脉冲响应音频文件。该文件的 FFT 表明它非常尖锐。我想平滑音频文件,而不仅仅是它的情节,这样我就有了一个更平滑的 IR 文件。
我发现 a function 显示 FFT 图已平滑。这种平滑如何应用于实际的 FFT 数据而不仅仅是它的绘图?
[y,Fs] = audioread('test\test IR.wav');
function x_oct = smoothSpectrum(X,f,Noct)
%SMOOTHSPECTRUM Apply 1/N-octave smoothing to a frequency spectrum
%% Input checking
assert(isvector(X), 'smoothSpectrum:invalidX', 'X must be a vector.');
assert(isvector(f), 'smoothSpectrum:invalidF', 'F must be a vector.');
assert(isscalar(Noct), 'smoothSpectrum:invalidNoct', 'NOCT must be a scalar.');
assert(isreal(X), 'smoothSpectrum:invalidX', 'X must be real.');
assert(all(f>=0), 'smoothSpectrum:invalidF', 'F must contain positive values.');
assert(Noct>=0, 'smoothSpectrum:invalidNoct', 'NOCT must be greater than or equal to 0.');
assert(isequal(size(X),size(f)), 'smoothSpectrum:invalidInput', 'X and F must be the same size.');
%% Smoothing
% calculates a Gaussian function for each frequency, deriving a
% bandwidth for that frequency
x_oct = X; % initial spectrum
if Noct > 0 % don't bother if no smoothing
for i = find(f>0,1,'first'):length(f)
g = gauss_f(f,f(i),Noct);
x_oct(i) = sum(g.*X); % calculate smoothed spectral coefficient
end
% remove undershoot when X is positive
if all(X>=0)
x_oct(x_oct<0) = 0;
end
end
endfunction
function g = gauss_f(f_x,F,Noct)
% GAUSS_F calculate frequency-domain Gaussian with unity gain
%
% G = GAUSS_F(F_X,F,NOCT) calculates a frequency-domain Gaussian function
% for frequencies F_X, with centre frequency F and bandwidth F/NOCT.
sigma = (F/Noct)/pi; % standard deviation
g = exp(-(((f_x-F).^2)./(2.*(sigma^2)))); % Gaussian
g = g./sum(g); % normalise magnitude
endfunction
% take fft
Y = fft(y);
% keep only meaningful frequencies
NFFT = length(y);
if mod(NFFT,2)==0
Nout = (NFFT/2)+1;
else
Nout = (NFFT+1)/2;
end
Y = Y(1:Nout);
f = ((0:Nout-1)'./NFFT).*Fs;
% put into dB
Y = 20*log10(abs(Y)./NFFT);
% smooth
Noct = 12;
Z = smoothSpectrum(Y,f,Noct);
% plot
semilogx(f,Y,'LineWidth',0.7,f,Z,'LineWidth',2.2);
xlim([20,20000])
grid on
PS。我有 Octave GNU,所以我没有 Matlab 工具箱可用的功能。
Here is the test IR audio file.
我想我找到了。由于音频文件(实数)的FFT是对称的,两边实部相同,虚部相反,所以我想到了这样做:
- 采用 FFT,保留一半,并应用平滑函数而不将幅度转换为 dB
- 然后复制平滑后的 FFT,并仅反转虚部
- 合并这两个部分,这样我就有了与开始时相同的对称 FFT,但现在它被平滑了
- 对此应用逆 FFT 并取实部并将其写入文件。
代码如下:
[y,Fs] = audioread('test IR.wav');
function x_oct = smoothSpectrum(X,f,Noct)
x_oct = X; % initial spectrum
if Noct > 0 % don't bother if no smoothing
for i = find(f>0,1,'first'):length(f)
g = gauss_f(f,f(i),Noct);
x_oct(i) = sum(g.*X); % calculate smoothed spectral coefficient
end
% remove undershoot when X is positive
if all(X>=0)
x_oct(x_oct<0) = 0;
end
end
endfunction
function g = gauss_f(f_x,F,Noct)
sigma = (F/Noct)/pi; % standard deviation
g = exp(-(((f_x-F).^2)./(2.*(sigma^2)))); % Gaussian
g = g./sum(g); % normalise magnitude
endfunction
% take fft
Y = fft(y);
% keep only meaningful frequencies
NFFT = length(y);
if mod(NFFT,2)==0
Nout = (NFFT/2)+1;
else
Nout = (NFFT+1)/2;
end
Y = Y(1:Nout);
f = ((0:Nout-1)'./NFFT).*Fs;
% smooth
Noct = 12;
Z = smoothSpectrum(Y,f,Noct);
% plot
semilogx(f,Y,'LineWidth',0.7,f,Z,'LineWidth',2.2);
xlim([20,20000])
grid on
#Apply the smoothing to the actual data
Zreal = real(Z); # real part
Zimag_neg = Zreal - Z; # opposite of imaginary part
Zneg = Zreal + Zimag_neg; # will be used for the symmetric Z
# Z + its symmetry with same real part but opposite imaginary part
reconstructed = [Z ; Zneg(end-1:-1:2)];
# Take the real part of the inverse FFT
reconstructed = real(ifft(reconstructed));
#Write to file
audiowrite ('smoothIR.wav', reconstructed, Fs, 'BitsPerSample', 24);
似乎有效! :) 如果有知识渊博的人可以确认思想和代码是好的,那就太好了:)
我想平滑脉冲响应音频文件。该文件的 FFT 表明它非常尖锐。我想平滑音频文件,而不仅仅是它的情节,这样我就有了一个更平滑的 IR 文件。 我发现 a function 显示 FFT 图已平滑。这种平滑如何应用于实际的 FFT 数据而不仅仅是它的绘图?
[y,Fs] = audioread('test\test IR.wav');
function x_oct = smoothSpectrum(X,f,Noct)
%SMOOTHSPECTRUM Apply 1/N-octave smoothing to a frequency spectrum
%% Input checking
assert(isvector(X), 'smoothSpectrum:invalidX', 'X must be a vector.');
assert(isvector(f), 'smoothSpectrum:invalidF', 'F must be a vector.');
assert(isscalar(Noct), 'smoothSpectrum:invalidNoct', 'NOCT must be a scalar.');
assert(isreal(X), 'smoothSpectrum:invalidX', 'X must be real.');
assert(all(f>=0), 'smoothSpectrum:invalidF', 'F must contain positive values.');
assert(Noct>=0, 'smoothSpectrum:invalidNoct', 'NOCT must be greater than or equal to 0.');
assert(isequal(size(X),size(f)), 'smoothSpectrum:invalidInput', 'X and F must be the same size.');
%% Smoothing
% calculates a Gaussian function for each frequency, deriving a
% bandwidth for that frequency
x_oct = X; % initial spectrum
if Noct > 0 % don't bother if no smoothing
for i = find(f>0,1,'first'):length(f)
g = gauss_f(f,f(i),Noct);
x_oct(i) = sum(g.*X); % calculate smoothed spectral coefficient
end
% remove undershoot when X is positive
if all(X>=0)
x_oct(x_oct<0) = 0;
end
end
endfunction
function g = gauss_f(f_x,F,Noct)
% GAUSS_F calculate frequency-domain Gaussian with unity gain
%
% G = GAUSS_F(F_X,F,NOCT) calculates a frequency-domain Gaussian function
% for frequencies F_X, with centre frequency F and bandwidth F/NOCT.
sigma = (F/Noct)/pi; % standard deviation
g = exp(-(((f_x-F).^2)./(2.*(sigma^2)))); % Gaussian
g = g./sum(g); % normalise magnitude
endfunction
% take fft
Y = fft(y);
% keep only meaningful frequencies
NFFT = length(y);
if mod(NFFT,2)==0
Nout = (NFFT/2)+1;
else
Nout = (NFFT+1)/2;
end
Y = Y(1:Nout);
f = ((0:Nout-1)'./NFFT).*Fs;
% put into dB
Y = 20*log10(abs(Y)./NFFT);
% smooth
Noct = 12;
Z = smoothSpectrum(Y,f,Noct);
% plot
semilogx(f,Y,'LineWidth',0.7,f,Z,'LineWidth',2.2);
xlim([20,20000])
grid on
PS。我有 Octave GNU,所以我没有 Matlab 工具箱可用的功能。
Here is the test IR audio file.
我想我找到了。由于音频文件(实数)的FFT是对称的,两边实部相同,虚部相反,所以我想到了这样做:
- 采用 FFT,保留一半,并应用平滑函数而不将幅度转换为 dB
- 然后复制平滑后的 FFT,并仅反转虚部
- 合并这两个部分,这样我就有了与开始时相同的对称 FFT,但现在它被平滑了
- 对此应用逆 FFT 并取实部并将其写入文件。
代码如下:
[y,Fs] = audioread('test IR.wav');
function x_oct = smoothSpectrum(X,f,Noct)
x_oct = X; % initial spectrum
if Noct > 0 % don't bother if no smoothing
for i = find(f>0,1,'first'):length(f)
g = gauss_f(f,f(i),Noct);
x_oct(i) = sum(g.*X); % calculate smoothed spectral coefficient
end
% remove undershoot when X is positive
if all(X>=0)
x_oct(x_oct<0) = 0;
end
end
endfunction
function g = gauss_f(f_x,F,Noct)
sigma = (F/Noct)/pi; % standard deviation
g = exp(-(((f_x-F).^2)./(2.*(sigma^2)))); % Gaussian
g = g./sum(g); % normalise magnitude
endfunction
% take fft
Y = fft(y);
% keep only meaningful frequencies
NFFT = length(y);
if mod(NFFT,2)==0
Nout = (NFFT/2)+1;
else
Nout = (NFFT+1)/2;
end
Y = Y(1:Nout);
f = ((0:Nout-1)'./NFFT).*Fs;
% smooth
Noct = 12;
Z = smoothSpectrum(Y,f,Noct);
% plot
semilogx(f,Y,'LineWidth',0.7,f,Z,'LineWidth',2.2);
xlim([20,20000])
grid on
#Apply the smoothing to the actual data
Zreal = real(Z); # real part
Zimag_neg = Zreal - Z; # opposite of imaginary part
Zneg = Zreal + Zimag_neg; # will be used for the symmetric Z
# Z + its symmetry with same real part but opposite imaginary part
reconstructed = [Z ; Zneg(end-1:-1:2)];
# Take the real part of the inverse FFT
reconstructed = real(ifft(reconstructed));
#Write to file
audiowrite ('smoothIR.wav', reconstructed, Fs, 'BitsPerSample', 24);
似乎有效! :) 如果有知识渊博的人可以确认思想和代码是好的,那就太好了:)