[speech processing] speech signal denoising based on Matlab GUI Hanning window fir notch filter [including Matlab source code 1711]

One 、 Introduction to speech processing

1 Characteristics of voice signal
Through the observation and analysis of a large number of speech signals, it is found that , Voice signal mainly has the following two characteristics ：
① In the frequency domain , The spectral components of speech signals are mainly concentrated in 300～3400Hz Within the scope of . Take advantage of this feature , An anti aliasing band-pass filter can be used to extract the frequency components of the speech signal in this range , Then press 8kHz The voice signal is sampled at the sampling rate of , You can get discrete speech signals .
② In the time domain , The voice signal has “ Short term ” Characteristics , That is, in general , The characteristics of speech signal change with time , But in a short time interval , The voice signal remains stable . In the voiced segment, it shows the characteristics of periodic signal , In the unvoiced segment, it shows the characteristics of random noise .

2 Voice signal acquisition
Before digitizing the voice signal , Anti aliasing pre filtering must be carried out first , There are two purposes of pre filtering ：① Suppress the frequency exceeding in each domain component of input signal guidance fs/2 All the components of (fs Is the sampling frequency ), To prevent aliasing interference .② Inhibition 50Hz Power frequency interference of power supply . such , The prefilter must be a bandpass filter , Set on it 、 The lower cut-off color ratio is fH and fL, For most speech codecs ,fH=3400Hz、fL＝60~100Hz、 The sampling rate is fs＝8kHz; For Ding speech recognition , When used for telephone users , The index is the same as that of speech codec . When used in occasions with high or high requirements fH＝4500Hz or 8000Hz、fL＝60Hz、fs＝10kHz or 20kHz.
In order to change the original analog voice signal into digital signal , It must go through two steps of sampling and quantization , Thus, the digital speech signal with discrete time and amplitude is obtained . Sampling is also called sampling , Is the discretization of the signal in time , That is, according to a certain time interval △t In analog signal x(t) Take its instantaneous value point by point . The Nyquist theorem must be satisfied when sampling , Sampling frequency fs Sampling must be carried out at a speed more than twice the maximum frequency of the signal under test , It is realized by multiplying the sampling pulse and the analog signal .
In the process of sampling, attention should be paid to the selection of sampling interval and signal confusion ： The sampling interval of the analog signal should be determined first . How to choose △t Many technical factors need to be considered . generally speaking , The higher the sampling frequency , The denser the number of sampling points , The resulting discrete signal is closer to the original signal . But too high sampling frequency is not desirable , For fixed length （T） The signal of , Excessive amount of data collected （N=T/△t）, Add unnecessary calculation workload and storage space to the computer ; If the amount of data （N） limit , The sampling time is too short , It will result in some data information being excluded . The sampling frequency is too low , Sampling points are too far apart , Then the discrete signal is not enough to reflect the waveform characteristics of the original signal , The signal cannot be restored , Cause signal confusion . According to the sampling theorem , When the sampling frequency is greater than twice the bandwidth of the signal , The sampling process does not lose information , The original signal waveform can be reconstructed without distortion from the sampled signal by using the ideal filter . Quantization is the discretization of amplitude , That is, the vibration amplitude is expressed by binary quantization level . The quantization level changes in series , The actual vibration value is a continuous physical quantity . The specific vibration value is rounded to the nearest quantization level .
The speech signal is pre filtered and sampled , from A／D The converter is transformed into a two address digital code . This anti aliasing filter is usually made in an integrated block with analog-to-digital converter , So for now , The digital quality of voice signal is guaranteed .
After collecting the voice signal , The voice signal needs to be analyzed , Such as time domain analysis of speech signal 、 Spectrum analysis 、 Spectrogram analysis and noise filtering .

3 Speech signal analysis technology
Speech signal analysis is the premise and foundation of speech signal processing , Only by analyzing the parameters that can represent the essential characteristics of speech signal , It is possible to use these parameters for efficient voice communication 、 Speech synthesis and speech recognition [8]. and , The sound quality of speech synthesis is good or bad , The level of speech recognition rate , It also depends on the accuracy and accuracy of the speech signal bridge . Therefore, speech signal analysis plays an important role in the application of speech signal processing .
Throughout the whole process of speech analysis is “ Short term analysis technology ”. because , As a whole, the characteristics of speech signal and the parameters characterizing its essential characteristics change with time , So it's an unsteady process , It cannot be analyzed and processed with digital signal processing technology for processing unstable signals . however , Because different speech is the response of a certain shape of the vocal tract formed by the movement of human oral muscles , This kind of oral muscle movement is very slow relative to speech frequency , So on the other hand , Although the phonetic multiple sign has time-varying characteristics , But in a short time range ( It is generally believed that in 10～30ms In a short time ), Its characteristics remain basically unchanged, that is, relatively stable , Because it can be regarded as a quasi steady state process , That is, the speech signal has short-term stability . Therefore, any speech signal analysis and processing must be based on “ short-term ” On the basis of ． That is to say “ Short term analysis ”, The speech signal is divided into segments to analyze its characteristic parameters , Each paragraph is called a “ frame ”, The frame length is generally taken as 10～30ms. such , For the overall voice signal , The time series of characteristic parameters composed of characteristic parameters of each frame is analyzed .
According to the different properties of the analyzed parameters , Speech signal analysis can be divided into time domain analysis 、 Frequency domain analysis 、 Inverted domain analysis, etc ; The time domain analysis method is simple 、 A small amount of calculation 、 Clear physical meaning and other advantages , However, because the most important perceptual characteristics of speech signal are reflected in the power spectrum , The phase change only plays a small role , Therefore, compared with time domain analysis, frequency domain analysis is more important .

4 Time domain analysis of speech signal
The time domain analysis of speech signal is to analyze and extract the time domain parameters of speech signal . When performing speech analysis , The first and most intuitive thing is its time domain waveform . Speech signal itself is time domain signal , Therefore, time domain analysis is the earliest use , It is also the most widely used analysis method , This method directly uses the time-domain waveform of speech signal . Time domain analysis is usually used for the most basic parameter analysis and application , Such as speech segmentation 、 Preprocessing 、 Large classification, etc . The characteristics of this analysis method are ：① It means that the voice signal is more intuitive 、 The physical meaning is clear .② It's easy to implement 、 Less computation .③ Some important parameters of speech can be obtained .④ Only use general equipment such as oscilloscope , Easy to use, etc .
The time domain parameters of speech signal have short-term energy 、 Short time zero crossing rate 、 Short time white correlation function and short time average amplitude difference function, etc , This is a set of basic short-time parameters of speech signal , It should be applied in various speech signal digital processing technologies [6]. The square window or Hamming window is generally used in calculating these parameters .

5 Frequency domain analysis of speech signal
The frequency domain analysis of speech signal is to analyze the frequency domain characteristics of speech signal . In a broad sense , The frequency domain analysis of speech signal includes the spectrum of speech signal 、 Power spectrum 、 Cepstrum 、 Spectrum envelope analysis, etc , The commonly used frequency domain analysis method is band-pass filter bank method 、 Fourier transform method 、 Line prediction method, etc .

Two 、 Partial source code

function varargout = wwzl(varargin)
% WWZL M-file for wwzl.fig
%      WWZL, by itself, creates a new WWZL or raises the existing
%      singleton*.
%
%      H = WWZL returns the handle to a new WWZL or the handle to
%      the existing singleton*.
%
%      WWZL('CALLBACK',hObject,eventData,handles,...) calls the local
%      function named CALLBACK in WWZL.M with the given input arguments.
%
%      WWZL('Property','Value',...) creates a new WWZL or raises the
%      existing singleton*.  Starting from the left, property value pairs are
%      applied to the GUI before wwzl_OpeningFcn gets called.  An
%      unrecognized property name or invalid value makes property application
%      stop.  All inputs are passed to wwzl_OpeningFcn via varargin.
%
%      *See GUI Options on GUIDE's Tools menu.  Choose "GUI allows only one
%      instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES

% Edit the above text to modify the response to help wwzl

% Last Modified by GUIDE v2.5 19-Nov-2012 23:20:06

% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @wwzl_OpeningFcn, ...
                   'gui_OutputFcn',  @wwzl_OutputFcn, ...
                   'gui_LayoutFcn',  [] , ...
                   'gui_Callback',   []);
if nargin && ischar(varargin{
    1})
    gui_State.gui_Callback = str2func(varargin{
    1});
end

if nargout
    [varargout{
    1:nargout}] = gui_mainfcn(gui_State, varargin{
    :});
else
    gui_mainfcn(gui_State, varargin{
    :});
end
% End initialization code - DO NOT EDIT


% --- Executes just before wwzl is made visible.
function wwzl_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
% varargin   command line arguments to wwzl (see VARARGIN)

% Choose default command line output for wwzl
handles.output = hObject;

% Update handles structure
guidata(hObject, handles);

% UIWAIT makes wwzl wait for user response (see UIRESUME)
% uiwait(handles.my_storage);


% --- Outputs from this function are returned to the command line.
function varargout = wwzl_OutputFcn(hObject, eventdata, handles) 
% varargout  cell array for returning output args (see VARARGOUT);
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Get default command line output from handles structure
varargout{
    1} = handles.output;


% --- Executes on slider movement.
function slider5_Callback(hObject, eventdata, handles)
% hObject    handle to slider5 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'Value') returns position of slider
%        get(hObject,'Min') and get(hObject,'Max') to determine range of slider


% --- Executes during object creation, after setting all properties.
function slider5_CreateFcn(hObject, eventdata, handles)
% hObject    handle to slider5 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: slider controls usually have a light gray background.
if isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor',[.9 .9 .9]);
end


% --- Executes on button press in noise_in.
function noise_in_Callback(hObject, eventdata, handles)
% hObject    handle to noise_in (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

x=getappdata(handles.my_storage,'x');
n=getappdata(handles.my_storage,'n');
t=getappdata(handles.my_storage,'t');

if  get(handles.EN_open,'value')==1         % Turn on mode 
    Xn=x+n;
    axes(handles.axes2);
    plot(t,Xn);grid on;
elseif get(handles.EN_record,'value')==1    % Recording mode 
    n=getappdata(handles.my_storage,'n');   
    Xn=getappdata(handles.my_storage,'data1')+n; 
    axes(handles.axes2);
    plot(getappdata(handles.my_storage,'time'),Xn);grid on;
end
setappdata(handles.my_storage,'Xn',Xn);

% --- Executes on button press in pushbutton9.
function pushbutton9_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton9 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)


% --- Executes on selection change in noises.
function noises_Callback(hObject, eventdata, handles)
% hObject    handle to noises (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: contents = cellstr(get(hObject,'String')) returns noises contents as cell array
%        contents{
    get(hObject,'Value')} returns selected item from noises

Fn=str2num(get(handles.noise_f,'string'));  % from edit For information 
Mn=str2num(get(handles.noise_M,'string'));

if get(handles.EN_open,'value')==1      % Turn on mode 
    x=getappdata(handles.my_storage,'x');
    fs=getappdata(handles.my_storage,'fs');
elseif get(handles.EN_record,'value')==1    % Recording mode 
    x=getappdata(handles.my_storage,'data1');
    fs=getappdata(handles.my_storage,'Fs');
end

t=1:length(x);
shap=get(handles.noises,'value');
switch shap
    case 2
        n=(Mn*sin((2*pi*Fn/fs)*t))';       % Variable frequency AC noise 
    case 3
        n=wgn(length(x),1,0.005);       % white Gaussian noise 
    case 4
        n=Mn*randn(length(x),1);        % Random noise 
    otherwise
        warndlg(' Please select the noise type ',' Tips ');
end

3、 ... and 、 Running results

Four 、matlab Edition and references

1 matlab edition
2014a

2 reference
[1] Han Jiqing , Zhang Lei , Zheng tieran . Voice signal processing （ The first 3 edition ）[M]. tsinghua university press ,2019.
[2] Liu ruobian . Deep learning : Speech recognition technology practice [M]. tsinghua university press ,2019.
[3] Song Yunfei , Jiang zhancai , Wei Zhonghua . be based on MATLAB GUI Voice processing interface design [J]. Technology Information . 2013,(02)