I'm doing research in speech recognition and I'm trying to design a algorithm for speech recognition using neural network in Matlab. How can i define my structure for the network??
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2 Answers
Matlab has a Neural Network Toolbox. Maybe you can take a look at that?
2 Comments
Ankita
i m using that toolbox..but there is any any option to define input and output layer neuron manually.
Ronald Wildenberg
It's been quite a long time since I used this toolbox but I can remember something like this was possible. I don't remember exactly how, unfortunately...
Your question is very general. You should us give more specific details of what you want to do.
However, I give you some tips on using Matlab neural network toolbox out of my experience with License Plate Recognition using this toolbox. hope it helps.
1-You will need to know how many hidden layers you'll be using as well as number of your outputs. In my case, I specified 20 hidden layers, and 9 outputs.
2-You need to have a dataset first. You will be using this dataset for training your neural network. In case of LPR, I used a 90x50 matrix as my dataset. every row represented a digit, holding 50 numbers extracted from a digit's image.
3- You will need a targets matrix to map your dataset to known outputs(hence called training).
The following syntax defines the network mentioned:
net = newff(minmax(datasetNormalized'),[20 9],{'logsig' 'logsig'},'traingdx');
For training the network we write:
[net,tr]=train(net,datasetNormalized',T);
We give a new input to the network using the following code:
[dummy,b]=max(sim(net,m_normalized'));
b is the answer we are looking for here(output with most probability), Therefore we display it to user:
msgbox(['digit is: ' num2str(b)],'Digit recognized','help');
This is the full source code if you want to know exactly what my code is doing:
clc
clear
close all
numOfPhotos=90;
imgRows=100;
imgCols=50;
X=zeros(numOfPhotos,(imgRows*imgCols)/100);
%%Resize Images
% myresize(imgRows,imgCols);
%read train images
datasetIndex=0;
for i=1:numOfPhotos/10
for j=1:numOfPhotos/9
datasetIndex=datasetIndex+1;
im=imread(['resized_train_numbers\' num2str(i) ' (' num2str(j) ').jpg']);
im=im2bw(im,graythresh(im));
c=1;
for g=1:imgRows/10
for e=1:imgCols/10
s=sum(sum(im((g*10-9:g*10),(e*10-9:e*10))));
X(datasetIndex,c)=s;
c=c+1;
end
end
end
end
datasetNormalized=zeros(numOfPhotos,imgRows*imgCols/100);
%%Normalize dataset contents
minDataset=min(min(X));
maxDataset=max(max(X));
for i = 1:numOfPhotos
for j=1:imgRows*imgCols/100
datasetNormalized(i, j) = (X(i,j)-minDataset)/(maxDataset-minDataset);
end
end
%
%%Neural network part
% T=zeros(1,90);
% for i=1:90
% T(i)=ceil(i/10);
% end
T=zeros(9,90);
for j=1:90
i=ceil(j/10);
T(i,j)=1;
end
% net=newff(datasetNormalized',T,20);
net = newff(minmax(datasetNormalized'),[20 9],{'logsig' 'logsig'},'traingdx');
net.performFcn='sse';
net.trainParam.goal=0.01;
net.trainParam.show=20;
net.trainParam.epochs=100;
net.trainParam.mc=0.95;
% net.trainFcn='trainlm';
net.trainParam.min_grad=1e-12;
[net,tr]=train(net,datasetNormalized',T);
%Read input image for recognition
[name file]=uigetfile('*.jpg','Choose Plate Digit Image');
newImg=imread([file name]);
newImg=imresize(newImg,[imgRows imgCols]);
newImg=im2bw(newImg,graythresh(newImg));
figure,imshow(newImg);
m=zeros(1,imgRows*imgCols/100);
c=1;
for g=1:imgRows/10
for e=1:imgCols/10
s=sum(sum(newImg((g*10-9:g*10),(e*10-9:e*10))));
m(c)=s;
c=c+1;
end
end
%Normalize m contents
m_normalized=zeros(1,imgRows*imgCols/100);
for i=1:imgRows*imgCols/100
m_normalized(i)=(m(i)-min(m))/(max(m)-min(m));
end
[dummy,b]=max(sim(net,m_normalized'));
% b=round(sim(net,m_normalized'));
msgbox(['digit is: ' num2str(b)],'Digit recognized','help');
