目录

💥1 概述

📚2 运行结果

🎉3 参考文献

👨‍💻4 Matlab代码

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💥1 概述

谱相减减少背景（加性）噪声影响的最流行方法之一是谱相减。背景噪声是降低录音中语音质量和清晰度的最常见因素。该去噪算法旨在降低噪声水平而不影响语音信号质量。我们需要设计陷波参数均衡器，有选择地滤除噪声发生的频率。由于陷波滤波器也可以去除上述频率下的语音信号的分量，我们将应用峰值滤波器来在陷波滤波器的输出处增强语音信号。
在Matlab上实现。执行时生成并播放去噪信号。

📚2 运行结果

 

 

 

🎉3 参考文献

[1]向瑾,翟成瑞,杨卫,孟令军,张文栋.基于小波变换的音频信号去噪[J].微计算机信息,2007(35):85-86.

👨‍💻4 Matlab代码

主函数部分代码：

fprintf('--- Audio Signal Denoising using Spectral Subtraction ---\n\n');
%load noise sound example
[y,Fe]=audioread('sample.wav');
x=y(100000:end,1).';  %remove the beginning of the sample
Nx=length(x);

%algorithm parameters
apriori_SNR=1;  %select 0 for aposteriori SNR estimation and 1 for apriori
alpha=0.05;      %only used if apriori_SNR=1
beta1=0.5;
beta2=1;
lambda=3;

%STFT parameters
NFFT=1024;
window_length=round(0.031*Fe); 
window=hamming(window_length);
window = window(:);
overlap=floor(0.45*window_length); %number of windows samples without overlapping

%Signal parameters
t_min=0.4;    %interval for learning the noise
t_max=1.00;   %spectrum (in second)

%construct spectrogram 
[S,F,T] = spectrogram(x+i*eps,window,window_length-overlap,NFFT,Fe); %put a short imaginary part to obtain two-sided spectrogram
[Nf,Nw]=size(S);

%Noisy spectrum extraction
t_index=find(T>t_min & T<t_max);
absS_noise=abs(S(:,t_index)).^2;
noise_spectrum=mean(absS_noise,2); %average spectrum of the noise 
noise_specgram=repmat(noise_spectrum,1,Nw);

%Estimate SNR
absS=abs(S).^2;
SNR_est=max((absS./noise_specgram)-1,0); 
if apriori_SNR==1
    SNR_est=filter((1-alpha),[1 -alpha],SNR_est);
end  

%Compute Attenuation Map
an_lk=max((1-lambda*((1./(SNR_est+1)).^beta1)).^beta2,0); 
STFT=an_lk.*S;

%Compute Inverse STFT
ind=mod((1:window_length)-1,Nf)+1;
output_signal=zeros((Nw-1)*overlap+window_length,1);

for indice=1:Nw %Overlapp add technique
    left_index=((indice-1)*overlap) ;
    index=left_index+[1:window_length];
    temp_ifft=real(ifft(STFT(:,indice),NFFT));
    output_signal(index)= output_signal(index)+temp_ifft(ind).*window;
end