Matlab implementa archivos de audio WAV para calcular los parámetros de calidad del sonido: dBA, sonoridad, rugosidad, nitidez y fluctuación.

Enterprise 2023-09-29 23:42:28 views: null

1.dBA

        Primero lea el archivo WAV

[x,Fs] = audioread('pink.wav');   %读取音频文件

        Ventana de la señal en el dominio del tiempo

function [dBA,dBZ,t,windowTime] = analyzeSignal(x,Fs)
responseType = 'fast';
C = 55;
t = 1/Fs:1/Fs:length(x)/Fs;
%% 确定傅里叶窗的大小
if strcmp(responseType,'slow')
   duration = 1.0;
else
   duration = 0.125;
end
N = ceil(duration*Fs);
N = 2^nextpow2(N);
%% 确定信号的dBA
windowStart = [1:N:(length(x)-N)];
dBA = zeros(length(windowStart),1);
dBZ = zeros(length(windowStart),1);
windowTime = t(windowStart+round((N-1)/2));
for i = [1:length(windowStart)]
   [dBA(i),dBZ(i)] = estimateLevel(x(windowStart(i)-1+[1:N]),Fs,C);
end

        Llame a la subfunción para calcular dBA

function [dBA,dBZ] = estimateLevel(x,Fs,C)
X = abs(fft(x));
%% 避免产生对数取0的情况
X(find(X == 0)) = 1e-17;
%% 保证奈奎斯特采样定律
f = (Fs/length(X))*[0:(length(X)-1)];
ind = find(f<Fs/2); f = f(ind); X = X(ind);
%% 通过A-weighting滤波器实现dBA计算
A = filterA(f);
XA = A'.*X;
%Z-weighting filter
Z = zeros(length(f),1);
Z(1:end) = 1;
XZ = Z.*X;
%% 用能量计算dBA
totalEnergy = sum(XA.^2)/length(XA);
meanEnergy = totalEnergy/((1/Fs)*length(x));
dBA = 10*log10(meanEnergy)+C;

totalEnergy1 = sum(XZ.^2)/length(XZ);
meanEnergy1 = totalEnergy1/((1/Fs)*length(x));
dBZ = 10*log10(meanEnergy1)+C;

        Subfunción de filtro ponderado A

function A = filterA(f,plotFilter)
%% Define filter coefficients.
c1 = 3.5041384e16;
c2 = 20.598997^2;
c3 = 107.65265^2;
c4 = 737.86223^2;
c5 = 12194.217^2;

%% Evaluate A-weighting filter.
f(find(f == 0)) = 1e-17;
f = f.^2; num = c1*f.^4;
den = ((c2+f).^2) .* (c3+f) .* (c4+f) .* ((c5+f).^2);
A = num./den;

%% 画dBA计权图.
if exist('plotFilter') & plotFilter
    
   % Plot using dB scale.
   figure(2); clf;
   semilogx(sqrt(f),10*log10(A));
   title('A-weighting Filter');
   xlabel('Frequency (Hz)');
   ylabel('Magnitude (dB)');
   xlim([10 100e3]); grid on;
   ylim([-70 10]);
   
   % Plot using linear scale.
   figure(3); plot(sqrt(f),A);
   title('A-weighting Filter');
   xlabel('Frequency (Hz)');
   ylabel('Amplitude');
   xlim([0 44.1e3/2]); grid on;

end

2. sonoridad

        Primero determine el tamaño de la ventana.

function [Loudness,Sharpness,Roughness,Fluctuation] = frameToCalculate(x,Fs);
%% 划分时域帧的长度
responseType = 'fast';
%避免输入信号不存在的情况
if ~exist('x')
   [x,Fs] = audioread();
   t = (1/Fs)*[0:(length(x)-1)]; t = t+81;
else
   t = (1/Fs)*[0:(length(x)-1)];
end
%根据fast和slow进行时间帧的划分
if strcmp(responseType,'slow')
   duration = 1;
else
   duration = 0.125;
end
N = ceil(duration*Fs);
N = 2^nextpow2(N);
% Estimate signal level (within each windowed segment).
windowStart = [1:N:(length(x)-N)];
Loudness = zeros(length(windowStart),1);
Sharpness = zeros(length(windowStart),1);
Roughness = zeros(length(windowStart),1);
Fluctuation = zeros(length(windowStart),1);
windowTime = t(windowStart+round((N-1)/2));
%% 按照划分的时间帧，分帧计算每帧的平均响度
for i = [1:length(windowStart)]
   Loudness(i) = estimateLoudness(x(windowStart(i)-1+[1:N]),Fs);
   Sharpness(i) = estimateSharpness(x(windowStart(i)-1+[1:N]),Fs);
   Roughness(i) = estimateRoughness(x(windowStart(i)-1+[1:N]),Fs);
   Fluctuation(i) = estimateFluctuation(x(windowStart(i)-1+[1:N]),Fs);
end
end

        Este artículo utiliza el modelo de Zwicker para calcular el volumen total a través del volumen característico de 24Barks:

 

 

function loudness = estimateLoudness(x,Fs)
%% 24Barks划分
fc = [20 100 200 300 400 510 630 770 920 1080 1270 1480 1720 2000 2320 2700 3150 3700 4400 5300 6400 7700 9500 12000;
    50 150 250 350 450 570 700 840 1000 1170 1370 1600 1850 2150 2500 2900 3400 4000 4800 5800 7000 8500 10500 13500;
    100 200 300 400 510 630 770 920 1080 1270 1480 1720 2000 2320 2700 3150 3700 4400 5300 6400 7700 9500 12000 15500];
BandWidth = [80 100 100 100 110 120 140 150 160 190 210 240 280 320 380 450 550 700 900 1100 1300 1800 2500 3500];
%% 中心频带率，有何意义
z = zeros(1,length(fc(2,:)));
for i = 1:length(z)
    z(i) = 13.*atan(0.76*fc(2,i)/1000)+3.5.*(atan(fc(2,i)/1000)).^2; %?
end
%% 听阈下限声压级，用getData在听阈曲线上估计出来
Lspl = [48.72457794 28.15401982 16.78690581 10.97541703 7.18374514 3.645302897 1.116392826 -0.152013044 0.093691208 -0.165558761 -0.9278816 -1.94268155 -2.95710523 -4.475730591 -6.751034742 -7.765458422 -8.528157532 -8.030352439 -5.516869434 -1.490781387 4.047911702 6.813119278 8.569170952 11.83632259];
%% Calculate magnitude of FFT.
X = abs(fft(x));
X(find(X == 0)) = 1e-17;%避免产生对数取0的情况
% Retain frequencies below Nyquist rate.
f = (Fs/length(X))*[0:(length(X)-1)];
ind = find(f<Fs/2); f = f(ind); X = X(ind);
%% 按bark进行声压级计算
SPL = zeros(1,24);
for i = 1:24
    location = find(f>=fc(1,i)&f<=fc(3,i));
    if ~isempty(location)
        totalEnergy = sum(X(location).^2)/length(location);
        SPL(i) = 10*log10(totalEnergy/((1/Fs)*length(x)))+55;
    else
        SPL(i) = 0;
    end
end
%% 按Bark进行响度计算
N = zeros(1,length(Lspl));
N = 0.08.*((10.^(Lspl/10)).^0.23).*((0.5+0.5.*10.^((SPL-Lspl)/10)).^0.23-1);%得到24bark响度分布图
loudness = sum(N);
end

3. Nitidez

        Elige también el modelo Zwicker

 

function sharpness = estimateSharpness(x,Fs)
%% 24Barks划分
fc = [20 100 200 300 400 510 630 770 920 1080 1270 1480 1720 2000 2320 2700 3150 3700 4400 5300 6400 7700 9500 12000;
    50 150 250 350 450 570 700 840 1000 1170 1370 1600 1850 2150 2500 2900 3400 4000 4800 5800 7000 8500 10500 13500;
    100 200 300 400 510 630 770 920 1080 1270 1480 1720 2000 2320 2700 3150 3700 4400 5300 6400 7700 9500 12000 15500];
BandWidth = [80 100 100 100 110 120 140 150 160 190 210 240 280 320 380 450 550 700 900 1100 1300 1800 2500 3500];
%% 中心频带率，有何意义？
z = zeros(1,length(fc(2,:)));
for i = 1:length(z)
    z(i) = 13.*atan(0.76*fc(2,i)/1000)+3.5.*(atan(fc(2,i)/1000)).^2; %?
end
%% 根据中心频带率z加上响度计权函数
gz = zeros(1,length(z));
for i = 1:length(z)
    if z(i)<=16
        gz(i) = z(i);
    else
        gz(i) = 0.06.*exp(0.17*z(i));
    end
end
%% 听阈下限声压级，用getData在听阈曲线上估计出来
Lspl = [48.72457794 28.15401982 16.78690581 10.97541703 7.18374514 3.645302897 1.116392826 -0.152013044 0.093691208 -0.165558761 -0.9278816 -1.94268155 -2.95710523 -4.475730591 -6.751034742 -7.765458422 -8.528157532 -8.030352439 -5.516869434 -1.490781387 4.047911702 6.813119278 8.569170952 11.83632259];
%% Calculate magnitude of FFT.
X = abs(fft(x));
X(find(X == 0)) = 1e-17;%避免产生对数取0的情况
% Retain frequencies below Nyquist rate.
f = (Fs/length(X))*[0:(length(X)-1)];
ind = find(f<Fs/2); f = f(ind); X = X(ind);
%% 按bark进行声压级计算
SPL = zeros(1,24);
for i = 1:24
    location = find(f>=fc(1,i)&f<=fc(3,i));
    if ~isempty(location)
        totalEnergy = sum(X(location).^2)/length(location);
        SPL(i) = 10*log10(totalEnergy/((1/Fs)*length(x)))+55;
    else
        SPL(i) = 0;
    end
end
%% 按Bark进行响度计算
N = zeros(1,length(Lspl));
N = 0.08.*((10.^(Lspl/10)).^0.23).*((0.5+0.5.*10.^((SPL-Lspl)/10)).^0.23-1);%得到24bark响度分布图
%% 计算sharpness
sharpness = 0.104.*sum(N.*gz.*z)./sum(N);
end

4. Rugosidad

        Fórmula de cálculo de rugosidad

 Dado que la profundidad del enmascaramiento no se puede describir mediante una fórmula matemática, aquí simplemente se reemplaza por un coeficiente multiplicado por el volumen (espero que puedas enseñarme un método de cálculo numérico más preciso).

function roughness = estimateRoughness(x,Fs)
%% 24Barks划分
fc = [20 100 200 300 400 510 630 770 920 1080 1270 1480 1720 2000 2320 2700 3150 3700 4400 5300 6400 7700 9500 12000;
    50 150 250 350 450 570 700 840 1000 1170 1370 1600 1850 2150 2500 2900 3400 4000 4800 5800 7000 8500 10500 13500;
    100 200 300 400 510 630 770 920 1080 1270 1480 1720 2000 2320 2700 3150 3700 4400 5300 6400 7700 9500 12000 15500];
BandWidth = [80 100 100 100 110 120 140 150 160 190 210 240 280 320 380 450 550 700 900 1100 1300 1800 2500 3500];
%% 中心频带率
z = zeros(1,length(fc(2,:)));
for i = 1:length(z)
    z(i) = 13.*atan(0.76*fc(2,i)/1000)+3.5.*(atan(fc(2,i)/1000)).^2; %
end
%% 听阈下限声压级，用getData在听阈曲线上估计出来
Lspl = [48.72457794 28.15401982 16.78690581 10.97541703 7.18374514 3.645302897 1.116392826 -0.152013044 0.093691208 -0.165558761 -0.9278816 -1.94268155 -2.95710523 -4.475730591 -6.751034742 -7.765458422 -8.528157532 -8.030352439 -5.516869434 -1.490781387 4.047911702 6.813119278 8.569170952 11.83632259];
%% Calculate magnitude of FFT.
X = abs(fft(x));
X(find(X == 0)) = 1e-17;%避免产生对数取0的情况
% Retain frequencies below Nyquist rate.
f = (Fs/length(X))*[0:(length(X)-1)];
ind = find(f<Fs/2); f = f(ind); X = X(ind);
%% 按bark进行声压级计算
SPL = zeros(1,24);
for i = 1:24
    location = find(f>=fc(1,i)&f<=fc(3,i));
    if ~isempty(location)
        totalEnergy = sum(X(location).^2)/length(location);
        SPL(i) = 10*log10(totalEnergy/((1/Fs)*length(x)))+55;
    else
        SPL(i) = 0;
    end
end
%% 按Bark进行粗糙度计算
N = zeros(1,length(Lspl));
N = 0.08.*((10.^(Lspl/10)).^0.23).*((0.5+0.5.*10.^((SPL-Lspl)/10)).^0.23-1);%得到24bark响度分布图
fmod = 300/1000;  %调制声频率
k = 0.01;   %掩蔽深度转换为响度的系数
L = k.*N;   %掩蔽深度
roughness = 0.3.*fmod.*sum(L);
end

5. Volatilidad

Según el modelo de cálculo.

function fluctuation = estimateFluctuation(x,Fs)
%% 24Barks划分
fc = [20 100 200 300 400 510 630 770 920 1080 1270 1480 1720 2000 2320 2700 3150 3700 4400 5300 6400 7700 9500 12000;
    50 150 250 350 450 570 700 840 1000 1170 1370 1600 1850 2150 2500 2900 3400 4000 4800 5800 7000 8500 10500 13500;
    100 200 300 400 510 630 770 920 1080 1270 1480 1720 2000 2320 2700 3150 3700 4400 5300 6400 7700 9500 12000 15500];
BandWidth = [80 100 100 100 110 120 140 150 160 190 210 240 280 320 380 450 550 700 900 1100 1300 1800 2500 3500];
%% 中心频带率
z = zeros(1,length(fc(2,:)));
for i = 1:length(z)
    z(i) = 13.*atan(0.76*fc(2,i)/1000)+3.5.*(atan(fc(2,i)/1000)).^2; %?
end
%% 听阈下限声压级，用getData在听阈曲线上估计出来
Lspl = [48.72457794 28.15401982 16.78690581 10.97541703 7.18374514 3.645302897 1.116392826 -0.152013044 0.093691208 -0.165558761 -0.9278816 -1.94268155 -2.95710523 -4.475730591 -6.751034742 -7.765458422 -8.528157532 -8.030352439 -5.516869434 -1.490781387 4.047911702 6.813119278 8.569170952 11.83632259];
%% Calculate magnitude of FFT.
dBZ(dBZ == 0) = 1e-17;%避免产生对数取0的情况
%% 按bark进行声压级计算
SPL = zeros(1,24);
for i = 1:24
    location = find(f>=fc(1,i)&f<=fc(3,i));
    if ~isempty(location)
        SPL(i) = sum(X(location))/length(location);
    else
        SPL(i) = 0;
    end
end
%% 按Bark进行响度计算
N = zeros(1,length(Lspl));
N = 0.08.*((10.^(Lspl/10)).^0.23).*((0.5+0.5.*10.^((SPL-Lspl)/10)).^0.23-1);%得到24bark响度分布图
k = 0.01;   %掩蔽深度转换为响度的系数
L = k.*N;   %掩蔽深度
fmod = 300/1000;  %调制频率
fluctuation = 0.008.*sum(L)/((fmod/4)+(4/fmod));
end

(Aún no se ha encontrado un modelo matemático adecuado para el cálculo de la profundidad de enmascaramiento)

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Origin blog.csdn.net/YGZ11113/article/details/127637222
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