how to train neural network using 3D matrix or some 2D matrix

I am trying to make simple sound source localization code, each wave file passed through 64 gammatone filer channel and then enframed to 20 ms frame with 10 ms overlap , ITD and ILD have calculated for frame ,the result is a matrix (64(number of channels)x (number of frameswhich differs depending on wave length) ) for ITD and another one for ILD, the grid azimuth consists of (,+10,-10,+20 ,-20 ,...,+80,-80),,now I have a problem how to organize the data to be input to the neural network as 10 ITD matrices and 10 ILD matrices for each azimuth

Answer 1

[Greg Heath]

I cannot understand your post. However, if you have N examples of I-dimensional input column vectors and the corresponding N examples of O-dimensional target column vectors, then

 [ I N ] = size(input)
 [ O N ] = size(target)

and the simplest code is (tr is the training record)

 net     = fitnet;
 [ net tr output error  ] = train(net, input,target);
 NMSE = mse(error)/mean(var(target',1))   % Normalized mean-square error in [ 0,1 ]

For details

 tr = tr % No semicolon

Hope this helps.

Thank you for formally accepting my answer

Greg

P.S. If single inputs and/or outputs are not in column vector form, columnize the corresponding rxc matrix using the columnization operator (:). Then combine to obtain the format above (e.g., I = rxc)