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①Matlab语音处理（进阶版）
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在语音去噪中最常用的方法是谱减法，谱减法是一种发展较早且应用较为成熟的语音去噪算法，该算法利用加性噪声与语音不相关的特点，在假设噪声是统计平稳的前提下，用无语音间隙测算到的噪声频谱估计值取代有语音期间噪声的频谱，与含噪语音频谱相减，从而获得语音频谱的估计值。谱减法具有算法简单、运算量小的特点，便于实现快速处理，往往能够获得较高的输出信噪比，所以被广泛采用。该算法经典形式的不足之处是处理后会产生具有一定节奏性起伏、听上去类似音乐的“音乐噪声”。
function fwseg_dist_noise_AI= AIST(cleanFile, enhancedFile,noiseFile)
% ----------------------------------------------------------------------
% Articulation index_short time(AIST)Objective Speech Quality Measure
% This function implements the AIST Measure
% Usage:AIST=AIST(clean.wav,enhanced.wav,noise.wav)
% clean.wav.wav-clean input file in.wav format
% enhanced.wav-enhanced output file in .wav format
% noise.wav- noise file which is noisy-clean
% AI_ST --computed AIST
% Note that the AIST measure is limited in the range[0,1].
% Example call: AI_ST = AIST(‘sp04.wav’,‘sp04_babble_sn10.wav’,’sp04_babble_sn10.wav-sp04.wav’)
% SNR=Xhat2/D2
% ----------------------------------------------------------------------
if nargin~=3
fprintf(‘USAGE: AIST=AIST(cleanFile.wav, enhancedFile.wav,noiseFile)
’);
fprintf(‘For more help, type: help comp_fwseg
’);
return;
end
[data0, Srate0]= audioread(noiseFile);
[data1, Srate1]= audioread(cleanFile);
[data2, Srate2]= audioread(enhancedFile);
if ( Srate0~= Srate1)
error( ‘The three files do not match!
’);
end
len= min(min(length(data0), length( data1)), length( data2));
data0= data0( 1: len)+eps;
data1= data1( 1: len)+eps;
data2= data2( 1: len)+eps;
wss_dist_vec_noisy= fwseg_noise( data0, data1, data2,Srate1);
fwseg_dist_noise_AI=mean(wss_dist_vec_noisy);
% ----------------------------------------------------------------------
function distortion = fwseg_noise(noise_speech, clean_speech, processed_speech,sample_rate)
% ----------------------------------------------------------------------
% Check the length of the noisy,the clean and processed speech.Must be the
% same
% ----------------------------------------------------------------------
noise_length = length(noise_speech);
clean_length = length(clean_speech);
processed_length = length(processed_speech);
if (noise_length ~= clean_length | clean_length ~= processed_length)
disp(‘Error: Files must have same length.’);
return
end
% ----------------------------------------------------------------------
% Global Variables
Len=30;
% ----------------------------------------------------------------------
winlength = round(Lensample_rate/1000); % window length in samples
skiprate = floor(winlength/4); %window skip in samples
max_freq = sample_rate/2; %maximum bandwidth
num_crit = 25; % number of critical bands
USE_25=1;
n_fft = 2^nextpow2(2winlength);
n_fftby2 = n_fft/2; % FFT size/2
gamma=1; % power exponent
% ----------------------------------------------------------------------
%Critical Band Filter Definitions(Center Frequency and Bandwidths in Hz)
% ----------------------------------------------------------------------
cent_freq(1) = 50.0000; bandwidth(1) = 70.0000;
cent_freq(2) = 120.000; bandwidth(2) = 70.0000;
cent_freq(3) = 190.000; bandwidth(3) = 70.0000;
cent_freq(4) = 260.000; bandwidth(4) = 70.0000;
cent_freq(5) = 330.000; bandwidth(5) = 70.0000;
cent_freq(6) = 400.000; bandwidth(6) = 70.0000;
cent_freq(7) = 470.000; bandwidth(7) = 70.0000;
cent_freq(8) = 540.000; bandwidth(8) = 77.3724;
cent_freq(9) = 617.372; bandwidth(9) = 86.0056;
cent_freq(10) = 703.378; bandwidth(10) = 95.3398;
cent_freq(11) = 798.717; bandwidth(11) = 105.411;
cent_freq(12) = 904.128; bandwidth(12) = 116.256;
cent_freq(13) = 1020.38; bandwidth(13) = 127.914;
cent_freq(14) = 1148.30; bandwidth(14) = 140.423;
cent_freq(15) = 1288.72; bandwidth(15) = 153.823;
cent_freq(16) = 1442.54; bandwidth(16) = 168.154;
cent_freq(17) = 1610.70; bandwidth(17) = 183.457;
cent_freq(18) = 1794.16; bandwidth(18) = 199.776;
cent_freq(19) = 1993.93; bandwidth(19) = 217.153;
cent_freq(20) = 2211.08; bandwidth(20) = 235.631;
cent_freq(21) = 2446.71; bandwidth(21) = 255.255;
cent_freq(22) = 2701.97; bandwidth(22) = 276.072;
cent_freq(23) = 2978.04; bandwidth(23) = 298.126;
cent_freq(24) = 3276.17; bandwidth(24) = 321.465;
cent_freq(25) = 3597.63; bandwidth(25) = 346.136;
% ----------------------------------------------------------------------
% Set up the critical band filters.Note here that Gaussianly shaped
% filter are used.Also, the sum of the filter weights are equivalent
% for each critical band filter.Filter less than -30dB point of filter
% ----------------------------------------------------------------------
bw_min = bandwidth (1); % minimum critical bandwidth
min_factor = exp (-30.0 / (2.0 * 2.303)); % -30 dB point of filter
for i = 1:num_crit
f0 = (cent_freq (i) / max_freq) * (n_fftby2);
all_f0(i) = floor(f0);
bw = (bandwidth (i) / max_freq) * (n_fftby2);
norm_factor = log(bw_min) - log(bandwidth(i));
j = 0:1:n_fftby2-1;
crit_filter(i,:) = exp (-11 (((j - floor(f0)) https://blog.csdn.net/KeepingMatlab/article/details/bw).^2) + norm_factor);
crit_filter(i,:) = crit_filter(i,:).(crit_filter(i,:) > min_factor);
end
1 matlab版本
2014a
2 参考文献
[1]韩纪庆,张磊,郑铁然.语音信号处理（第3版）[M].清华大学出版社，2019.
[2]柳若边.深度学习:语音识别技术实践[M].清华大学出版社，2019.
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