Fix time-domain calculations of the optimal observation filter

The calculations for the time-domain optimal observation filters have been fixed.

There are some minor improvements in other fields such as:

• A bug in obsFiltTD() that screwed up the dimensions of the arrays when there was only one source.
• A bug in ptSrcFieldTD() that screwed up the dimensions of the arrays when there was only one source.
• The virtual microphone geometry generation function was heavily modified, and its interface has changed.

This merge closes the Closes #8 (closed).

Virtual Sensing/Remote Microphone Technique/MATLAB/Functions/obsFiltTD.m

@@ -211,7 +211,7 @@ function [O, Rme, Rmm, Ovec, RmeMtx, RmmMtx, condNum, mMtx, Omean, RmeMean, RmmM
         for mIdx = size(m, 2):-1:1
             % Calculate the cross-correlations between virtual and monitoring microphones
             for eIdx = size(e, 2):-1:1
-                corr = xcorr(m(:, mIdx, jIdx), e(:, eIdx, jIdx), filtLen, "unbiased");
+                corr = xcorr(m(:, mIdx, jIdx), e(:, eIdx, jIdx), filtLen);
 
                 Rme(:, mIdx, eIdx, jIdx) = corr(filtLen + 1:-1:2);
             end
@@ -220,11 +220,11 @@ function [O, Rme, Rmm, Ovec, RmeMtx, RmmMtx, condNum, mMtx, Omean, RmeMean, RmmM
             for mmIdx = mIdx:-1:1
                 % Auto-correlation matrices are Toeplitz symmetric
                 if mIdx == mmIdx
-                    corr = xcorr(m(:, mmIdx, jIdx), m(:, mmIdx, jIdx), filtLen, "unbiased");
+                    corr = xcorr(m(:, mmIdx, jIdx), m(:, mmIdx, jIdx), filtLen);
 
                     Rmm(:, :, mIdx, mmIdx, jIdx) = toeplitz(corr(filtLen + 1:-1:2));
                 else
-                    corr = xcorr(m(:, mIdx, jIdx), m(:, mmIdx, jIdx), filtLen, "unbiased");
+                    corr = xcorr(m(:, mIdx, jIdx), m(:, mmIdx, jIdx), filtLen);
 
                     % Cross-correlation matrices
                     for iIdx = filtLen-1:-1:0
@@ -250,12 +250,8 @@ function [O, Rme, Rmm, Ovec, RmeMtx, RmmMtx, condNum, mMtx, Omean, RmeMean, RmmM
         Ovec(:, :, jIdx) = RmeMtx(:, :, jIdx).'/(RmmMtx(:, :, jIdx) + beta * eye(size(RmmMtx, 1)));
     end
 
-    % "Split" observation filter vector to observation filters per monitoring and virtual microphone 
-    for jIdx = size(Ovec, 3):-1:1
-        for mIdx = size(m, 2):-1:1
-            O(:, :, mIdx, jIdx) = Ovec(:, mIdx:size(m, 2):end, jIdx);
-        end
-    end
+    % "Split" observation filter vector to observation filters per monitoring and virtual microphone
+    O = permute(reshape(Ovec, size(Ovec, 1), size(m, 2), filtLen, size(Ovec, 3)), [1, 3, 2, 4]);
 
     % ====================================================
     % Provide additional output arguments
@@ -282,9 +278,7 @@ function [O, Rme, Rmm, Ovec, RmeMtx, RmmMtx, condNum, mMtx, Omean, RmeMean, RmmM
         Oopt = RmeMtxMean.'/(RmmMtxMean + beta * eye(size(RmmMtxMean)));
 
         % Reshape
-        for mIdx = size(m, 2):-1:1
-            Omean(:, :, mIdx) = Oopt(:, mIdx:size(m, 2):end);
-        end
+        Omean = permute(reshape(Oopt, size(Oopt, 1), size(m, 2), filtLen), [1, 3, 2]);
     end
     
     % Mean cross-correlations between monitoring and virtual microphones over trials/sound field realisations