I'm trying to split an audio file into 30 millisecond disjoint intervals using Matlab. I have the following code at the moment:
clear all
close all
% load the audio file and get its sampling rate
[y, fs] = audioread('JFK_ES156.wav');
for m = 1 : 6000
[t(m), fs] = audioread('JFK_ES156.wav', [(m*(0.03)*fs) ((m+1)*(0.03)*fs)]);
end
But the problem is that I get the following error:
In an assignment A(I) = B, the number of elements in B and I
must be the same.
Error in splitting (line 12)
[t(m), fs] = audioread('JFK_ES156.wav', [(m*(0.03)*fs)
((m+1)*(0.03)*fs)]);
I don't see why there's a mismatch in the number of elements in B and I and how to solve this. How can I get past this error? Or is there just an easier way to split the audio file (maybe another function I don't know about or something)?
I think the easiest way to split audio is to just load it and use the vec2mat function. so you would have something like this;
[X,Fs] = audioread('JFK_ES156.wav');
%Calculate how many samples you need to capture 30ms of audio
matSize = Fs*0.3;
%Pay attention to that apostrophe. Makes sure samples are stored in columns
%rather than rows.
output = vec2mat(x,matSize)';
%You can now have your audio split up into the different columns of your matrix.
%You can call them by using the column calling command for matrices.
%Plot first 30ms of audio
plot(output(:,1));
%You can join the audio back together using this command.
output = output(:);
Hope that helps. Another good thing about this method is that it keeps all your data in one place!
Edit : One thing I thought of, you may get a problem with this depending on your vector size. But I think vec2mat actually zeroPads your vector. Not a big thing, but if you're moving back and forth between the two, then it might be a good idea to have another variable that stores the original length of your signal.
You should just use the variable y and reshape it to form your split audio. For example,
chunk_size = fs*0.03;
y_chunks = reshape(y, chunk_size, 6000);
That will give you a matrix with each column a 30 ms chunk. This code will also be faster than reading small segments from file in a loop.
As hiandbaii suggested you could also use cell array. Make sure you clear your existing variables before that. Not clearing the array t is probably the reason you got the error "Cell contents assignment to a non-cell array object."
Your original error is because you cannot assign a vector with scalar indexing. That is, 'm' is a scalar, but your audioread call is returning a vector. This is what the error says about mismatch in size of I and B. You could also fix that by making t a 2-D array and use an assignment like
[t(m,:), fs] =
It appears that each 30 ms segment is not equal to one sample. That would be the only case where your code works. i.e. 0.03*fs != 1.
You could try using cells instead.. i.e. replace t(m) with t{m}
Related
I have a very huge 3D matrix, the data was written into disk for future use. Writing the matrix into a bin is easy, reading it back however have some issue.
Write to bin:
z=repmat(complex(rand(5),rand(5)),[1 1 5])
z_imag = imag(z);
z_real = real(z);
adjacent = [z_real z_imag];
fileID = fopen('complex.bin','w');
fwrite(fileID,adjacent,'double')
And now, I try to read it back using memmapfile:
m = memmapfile('complex.bin', 'Offset', 0, 'Format', {'double' [5,5,5] 'x'});
complexValues = complex(m.Data(:).x(1,:), m.Data(:).x(2,:)); %this line doesn't work though, just for explanation's sake
It gave me an error saying that
Error using memmapfile/subsref (line 764) A subscripting operation on
the Data field attempted to create a comma-separated list. The
memmapfile class does not support the use of comma-separated lists
when subscripting.
I was referring to the solution here, the suggested solution used the reshape to shape the matrix beforehand (as contrast to my method above). I try to avoid using reshape in my code as I'm dealing with very huge data and that might computationally expensive and takes a long time. Is there an alternative/better way to do this?
Thanks in advance!
I have a dataset of n nifti (.nii) images. Ideally, I'd like to be able to get the value of the same voxel/element from each image, and apply a function to the n data points. I'd like to do this for each voxel/element across the whole image, so that I can reconvert the result back into .nii format.
I've used the Tools for NIfTI and ANALYZE image toolbox to load my images:
data(1)=load_nii('C:\file1.nii');
data(2)=load_nii('C:\file2.nii');
...
data(n)=load_nii('C:\filen.nii');
From which I obtain a struct object with each sub-field containing one loaded nifti. Each of these has a subfield 'img' corresponding to the image data I want to work on. The problem comes from trying to select a given xyz within each img field of data(1) to data(n). As I discovered, it isn't possible to select in this way:
data(:).img(x,y,z)
or
data(1:n).img(x,y,z)
because matlab doesn't support it. The contents of the first brackets have to be scalar for the call to work. The solution from googling around seems to be a loop that creates a temporary variable:
for z = 1:nz
for x = 1:nx
for y = 1:ny
for i=1:n;
points(i)=data(i).img(x,y,z);
end
[p1(x,y,z,:),~,p2(x,y,z)] = fit_data(a,points,b);
end
end
end
which works, but takes too long (several days) for a single set of images given the size of nx, ny, nz (several hundred each).
I've been looking for a solution to speed up the code, which I believe depends on removing those loops by vectorisation, preselecting the img fields (via getfield ?)and concatenating them, and applying something like arrayfun/cellfun/structfun, but i'm frankly a bit lost on how to do it. I can only think of ways to pre-select which themselves require loops, which seems to defeat the purpose of the exercise (though a solution with fewer loops, or fewer nested loops at least, might do it), or fun into the same problem that calls like data(:).img(x,y,z) dont work. googling around again is throwing up ways to select and concatenate fields within a struct, or a given field across multiple structs. But I can't find anything for my problem: select an element from a non-scalar sub-field in a sub-struct of a struct object (with the minimum of loops). Finally I need the output to be in the form of a matrix that the toolbox above can turn back into a nifti.
Any and all suggestions, clues, hints and help greatly appreciated!
You can concatenate images as a 4D array and use linear indexes to speed up calculations:
img = cat(4,data.img);
p1 = zeros(nx,ny,nz,n);
p2 = zeros(nx,ny,nz);
sz = ny*nx*nz;
for k = 1 : sz
points = img(k:sz:end);
[p1(k:sz:end),~,p2(k)] = fit_data(a,points,b);
end
I am trying to speed up a script that I have written in Matlab that dynamically allocates memory to a matrix (basicallly reads a line of data from a file and writes it into a matrix, then reads another line and allocates more memory for a larger matrix to store the next line). The reason I did this instead of preallocating memory using zeroes() or something was that I don't know the exact size the matrix needs to be to hold all of the data. I also don't know the maximum size of the matrix, so I can't just preallocate a max size and then get rid of memory that I didn't use. This was fine for small amounts of data, but now I need to scale my script up to read many millions of data points and this implementation of dynamic allocation is just much too slow.
So here is my attempt to speed up the script: I tried to allocate memory in large blocks using the zeroes function, then once the block is filled I allocate another large block. Here is some sample code:
data = [];
count = 0;
for ii = 1:num_filelines
if mod(count, 1000) == 0
data = [data; zeroes(1000)]; %after 1000 lines are read, allocate another 1000 line
end
data(ii, :) = line_read(file); %line_read reads a line of data from 'file'
end
Unfortunately this doesn't work, when I run it I get an error saying "Error using vertcat
Dimensions of matrices being concatenated are not consistent."
So here is my question: Is this method of allocating memory in large blocks actually any faster than incremental dynamic allocation, and also why does the above code not run? Thanks for the help.
What I recommend doing, if you know the number of lines and can just guess a large enough number of acceptable columns, use a sparse matrix.
% create a sparse matrix
mat = sparse(numRows,numCols)
A sparse matrix will not store all of the zero elements, it only stores pointers to indices that are non-zero. This can help save a lot of space. They are used and accessed the same as any other matrix. That is only if you really need it in a matrix format from the beginning.
If not, you can just do everything as a cell. Preallocate a cell array with as many elements as lines in your file.
data = cell(1,numLines);
% get matrix from line
for i = 1:numLines
% get matrix from line
data{i} = lineData;
end
data = cell2mat(data);
This method will put everything into a cell array, which can store "dynamically" and then be converted to a regular matrix.
Addition
If you are doing the sparse matrix method, to trim up your matrix once you are done, because your matrix will likely be larger than necessary, you can trim this down easily, and then cast it to a regular matrix.
[val,~] = max(sum(mat ~= 0,2));
mat(:,val:size(mat,2)) = [];
mat = full(mat); % use this only if you really need the full matrix
This will remove any unnecessary columns and then cast it to a full matrix that includes the 0 elements. I would not recommend casting it to a full matrix, as this requires a ton more space, but if you truly need it, use it.
UPDATE
To get the number of lines in a file easily, use MATLAB's perl interpretter
create a file called countlines.pl and paste in the two lines below
while (<>) {};
print $.,"\n";
Then you can run this script on your file as follows
numLines = str2double(perl('countlines.pl','data.csv'));
Problem solved.
From MATLAB forum thread here
remember it is always best to preallocate everything before hand, because technically when doing shai's method you are reallocating large amounts a lot, especially if it is a large file.
To solve your error, simply use this syntax when allocating
data = [data; zeroes(1000, size(data,2))];
You might want to read the first line outside the loop so you'll know the number of columns and make the first allocation for data.
If you want to stick to your code as written I would substitute your initialization of data, data = [] to
data = zeros(1,1000);
Keep in mind though the warning from #MZimmerman6: zeros(1000) generates a 1000 x 1000 array. You may want to change all of your zeros statements to zeros( ... ,Nc), where Nc = length of line in characters.
I have 50 matrices contained in one folder, all of dimension 181 x 360. How do I cycle through that folder and take an average of each corresponding data points across all 50 matrices?
If the matrices are contained within Matlab variables stored using save('filename','VariableName') then they can be opened using load('filename.mat').
As such, you can use the result of filesInDirectory = dir; to get a list of all your files, using a search pattern if appropriate, like files = dir('*.mat');
Next you can use your load command, and then whos to see which variables were loaded. You should consider storing these for ease clearing after each iteration of your loop.
Once you have your matrix loaded (one at a time), you can take averages as you need, probably summing a value across multiple loop iterations, then dividing by a total counter you've been measuring (using perhaps count = count + size(MatrixVar, dimension);).
If you need all of the matrices loaded at once, then you can modify the above idea, to load using a loop, then average outside of the loop. In this case, you may need to take care - but 50*181*360 isn't too bad I suspect.
A brief introduction to the load command can be found at this link. It talks mainly about opening one matrix, then plotting the values, but there are some comments about dealing with headers, if needed, and different ways in which you can open data, if load is insufficient. It doesn't talk about binary files, though.
Note on binary files, based on comment to OP's question:
If the file can be opened using
FID = fopen('filename.dat');
fread(FID, 'float');
then you can replace the steps referring to load above, and instead use a loop to find filenames using dir, open the matrices using fopen and fread, then average as needed, finally closing the files and clearing the matrices.
In this case, probably your file identifier is the only part you're likely to need to change during the loop (although your total will increase, if that's how you want to average your data)
Reshaping the matrix, or inverting it, might make the code clearer (which is good!), but might not be necessary depending on what you're trying to average - it may be that selecting only a subsection of the matrix is sufficient.
Possible example code?
Assuming that all of the files in the current directory are to be opened, and that no files are elsewhere, you could try something like:
listOfFiles = dir('*.dat');
for f = 1:size(listOfFiles,1)
FID = fopen(listOfFiles(f).name);
Data = fread(FID, 'float');
% Reshape if needed?
Total = Total + sum(Data(start:end,:)); % This might vary, depending on what you want to average etc.
Counter = Counter + (size(Data,1) * size(Data,2)); % This product will be the 181*360 you had in the matrix, in this case
end
Av = Total/Counter;
I am trying to use MATLAB in order to simulate a communications encoding and decoding mechanism. Hence all of the data will be 0's or 1's.
Initially I created a vector of a specific length and populated with 0's and 1's using
source_data = rand(1,8192)<.7;
For encoding I need to perform XOR operations multiple times which I was able to do without any issue.
For the decoding operation I need to implement the Gaussian Elimination method to solve the set of equations where I realized this vector representation is not very helpful. I tried to use strcat to append multiple 0's and 1's to a variable a using a for loop:
for i=1:8192
if(mod(i,2)==0)
a = strcat(a,'0');
else
a = strcat(a,'1');
end
i = i+1;
disp(i);
end
when I tried length(a) after this I found that the length was 16384, which is twice 8192. I am not sure where I am going wrong or how best to tackle this.
Did you reinitialize a before the example code? Sounds like you ran it twice without clearing a in between, or started with a already 8192 long.
Growing an array in a loop like this in Matlab is inefficient. You can usually find a vectorized way to do stuff like this. In your case, to get an 8192-long array of alternating ones and zeros, you can just do this.
len = 8192;
a = double(mod(1:len,2) == 0);
And logicals might be more suited to your code, so you could skip the double() call.
There are probably a few answer/questions here. Firstly, how can one go from an arbitrary vector containing {0,1} elements to a string? One way would be to use cellfun with the converter num2str:
dataDbl = rand(1,8192)<.7; %see the original question
dataStr = cellfun(#num2str, num2cell(dataDbl));
Note that cellfun concatenates uniform outputs.