[Emotion Recognition] Speech Emotion Recognition Based on Neural Network [With Matlab source code 544]

1. Introduction

BP network (Back Propagation), proposed by a team of scientists led by Rumelhart and McCelland in 1986, is a multi-layer feedforward network trained by error back propagation algorithm, and is currently one of the most widely used neural network models. The BP network can learn and store a large number of input-output pattern mapping relationships without revealing the mathematical equations describing this mapping relationship in advance.
In the history of the development of artificial neural networks, an effective algorithm for adjusting the connection weight of the hidden layer has not been found for a long time. Until the error back propagation algorithm (BP algorithm) was proposed, the problem of weight adjustment of multilayer feedforward neural networks for solving nonlinear continuous functions was successfully solved.

BP (Back Propagation) neural network, the learning process of error back propagation algorithm, consists of two processes: forward propagation of information and back propagation of error. Each neuron in the input layer is responsible for receiving input information from the outside and transmitting it to each neuron in the middle layer; the middle layer is the internal information processing layer and is responsible for information transformation. According to the demand for information change ability, the middle layer can be designed as a single hidden layer or Multi-hidden layer structure; the last hidden layer transmits the information of each neuron in the output layer, after further processing, completes a forward propagation process of learning, and the output layer outputs the information processing result to the outside world. When the actual output does not match the expected output, the error back propagation phase is entered. The error passes through the output layer, corrects the weights of each layer in the way of error gradient descent, and transmits it back to the hidden layer and the input layer layer by layer. The repeated process of forward propagation of information and back propagation of errors is a process of constant adjustment of the weights of each layer, as well as a process of neural network learning and training. This process continues until the error of the network output is reduced to an acceptable level, or preset Up to the set number of studies.

BP neural network model BP network model includes its input and output model, action function model, error calculation model and self-learning model.
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2 BP neural network model and its basic principles

3 BP_PID algorithm flow

Second, the source code

lc 
close all
clear all
load A_fear fearVec;
load F_happiness hapVec;
load N_neutral neutralVec;
load T_sadness sadnessVec;
load W_anger angerVec;
 trainsample(1:30,1:140)=angerVec(:,1:30)';
 trainsample(31:60,1:140)=hapVec(:,1:30)';
 trainsample(61:90,1:140)=neutralVec(:,1:30)';
 trainsample(91:120,1:140)=sadnessVec(:,1:30)';
 trainsample(121:150,1:140)=fearVec(:,1:30)';
  trainsample(1:30,141)=1;
   trainsample(31:60,141)=2;
   trainsample(61:90,141)=3;
   trainsample(91:120,141)=4; 
   trainsample(121:150,141)=5;
   testsample(1:20,1:140)=angerVec(:,31:50)';
  testsample(21:40,1:140)=hapVec(:,31:50)';
 testsample(41:60,1:140)=neutralVec(:,31:50)';
  testsample(61:80,1:140)=sadnessVec(:,31:50)';
  testsample(81:100,1:140)=fearVec(:,31:50)';
  testsample(1:20,141)=1;
   testsample(21:40,141)=2;
    testsample(41:60,141)=3;
    testsample(61:80,141)=4; 
    testsample(81:100,141)=5;
  class=trainsample(:,141);
sum=bpnn(trainsample,testsample,class);
figure(1)
bar(sum,0.5);
set(gca,'XTickLabel',{
    
    '生气','高兴','中性','悲伤','害怕'});
ylabel('识别率');
xlabel('五种基本情感');

p_train=trainsample(:,1:140)';
t_train=trainsample(:,141)';
p_test=testsample(:,1:140)';
t_test=testsample(:,141)';
sumpnn=pnn(p_train,t_train,p_test,t_test);
figure(2)
bar(sumpnn,0.5);
set(gca,'XTickLabel',{
    
    '生气','高兴','中性','悲伤','害怕'});
ylabel('识别率');
xlabel('五种基本情感');
sumlvq=lvq(trainsample,testsample,class);
function sum=bpnn(trainsample,testsample,class)
%输入参数：trainsample是训练样本,testsample是测试样本,class表示训练样本的类别，与trainsample中数据对应
%sum：五种基本情感的识别率
for i=1:140
    feature(:,i)= trainsample(:,i);
end
%特征值归一化
[input,minI,maxI] = premnmx( feature')  ;

%构造输出矩阵
s = length( class ) ;
output = zeros( s , 5  ) ;
for i = 1 : s 
   output( i , class( i )  ) = 1 ;
end

%创建神经网络
net = newff( minmax(input) , [10 5] , {
    
     'logsig' 'purelin' } , 'traingdx' ) ;   %创建前馈神经网络

%设置训练参数
net.trainparam.show = 50 ;
net.trainparam.epochs = 150 ;
net.trainparam.goal = 0.1 ;
net.trainParam.lr = 0.05 ;

%开始训练
net = train( net, input , output' ) ;

%读取测试数据
for i=1:140
    featuretest(:,i)= testsample(:,i);
end
 c=testsample(:,141);
%测试数据归一化
testInput = tramnmx(featuretest' , minI, maxI ) ;

%仿真
Y = sim( net , testInput ) 
sum=[0 0 0 0 0]; %每类情感正确识别个数
%统计识别正确样本数 
for i=1:20
    if Y(1,i)>Y(2,i)&&Y(1,i)>Y(3,i)&&Y(1,i)>Y(4,i)&&Y(1,i)>Y(5,i)
        sum(1)=sum(1)+1;
    end
    function sumlvq=lvq(trainsample,testsample,class)
P=trainsample(:,1:140)';
C=class';
T=ind2vec(C);
net=newlvq(minmax(P),20,[0.2 0.2 0.2 0.2 0.2],0.1); %创建lvq网络
w1=net.IW{
    
    1};
net.trainParam.epochs=100;
net=train(net,P,T);
y=sim(net,testsample(:,1:140)');
y3c=vec2ind(y);
sumlvq=[0 0 0 0 0]; %每类情感正确识别个数
%统计识别正确样本数 
for i=1:20
    if y3c(i)==1
        sumlvq(1)=sumlvq(1)+1;
    end
end
for i=21:40
    if y3c(i)==2
        sumlvq(2)=sumlvq(2)+1;
    end
end
for i=41:60
    if y3c(i)==3
        sumlvq(3)=sumlvq(3)+1;
    end
end
for i=61:80
    if y3c(i)==4
        sumlvq(4)=sumlvq(4)+1;
    end
end
for i=81:100
end

Three, running results

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Four, remarks