SNR in observation filter calculation feature and repository version update

A feature to add noise with a specified SNR value to the power spectral density matrix of the monitoring microphones is added to obsFilt() in MATLAB implementations.

Furthermore, the README.md files and project version have been updated.

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

@@ -3,7 +3,7 @@
 % Author: Achilles Kappis
 % e-mail: axilleaz@protonmail.com
 %
-% Date: 15/08/2024 (DD/MM/YYYY)
+% Date: 01/09/2024 (DD/MM/YYYY)
 %
 % Copyright: MIT
 % --------------------------------------------------
@@ -36,6 +36,17 @@
 %                               regularisation factor for all microphones.
 %                               [Default: 0]
 % 
+% snrVal [numeric] (Optional): This is the Root-Mean-Square (RMS)
+%                              Signal-to-Noise Ratio (SNR) in the
+%                              monitoring microphone signals. This must
+%                              be either a scalar indicating the common
+%                              SNR value of the microphone signals, or a
+%                              vector of dimensions Kx1, containing the SNR
+%                              values of each microphone. The values must
+%                              be real or Inf for the noiseless case. See
+%                              the Notes below for references on how this
+%                              value is calculated. [Default: Inf]
+% 
 % --------------------------------------------------
 % Output
 % 
@@ -59,12 +70,16 @@
 % --------------------------------------------------
 % Notes
 % 
+% - The SNR at the monitoring microphones calculation is based on the
+%   paper: "Combining the remote microphone technique with head-tracking
+%   for local active sound control" by W. Jung, S. J. Elliott and J. Cheer.
+% 
 % --------------------------------------------------
-function [oOpt, Sme, Smm, condNum] = obsFilt(Pe, Pm, srcCsd, regFacs)
+function [oOpt, Sme, Smm, condNum] = obsFilt(Pe, Pm, srcCsd, regFacs, snrVal)
     % ====================================================
     % Check for number of arguments
     % ====================================================
-    narginchk(2, 4);
+    narginchk(2, 5);
     nargoutchk(0, 4);
 
     % ====================================================
@@ -92,6 +107,21 @@ function [oOpt, Sme, Smm, condNum] = obsFilt(Pe, Pm, srcCsd, regFacs)
         regFacs = 0;
     end
 
+    if nargin > 4 && ~isempty(snrVal)
+        validateattributes(snrVal, "numeric", {'vector', 'real', 'nonnan', 'nonempty'}, mfilename, "Normalised Root-Mean-Square Singal-to-Noise Ratio of the monitoring microphones", 5);
+
+        % Make sure snrVal has correct dimensions
+        if ~isscalar(snrVal) && numel(snrVal) ~= size(Pm, 1)
+            error("SNR must be either a scalar or its length must match the number of monitoring microphones.");
+        end
+
+        if sum(snrVal == -Inf) > 0
+            error("SNR value cannot be equal to -Inf");
+        end
+    else
+        snrVal = Inf;
+    end
+
 
     % ====================================================
     % Calculate optimal filters
@@ -102,14 +132,24 @@ function [oOpt, Sme, Smm, condNum] = obsFilt(Pe, Pm, srcCsd, regFacs)
     Sme = Pe * tmpVal; % Virtual-Monitor mics cross-spectra
     Smm = Pm * tmpVal; % Monitor-Monitor mics cross-spectra
 
+    % Regularisation matrix
     if isscalar(regFacs)
-        regMat = eye(size(Smm)) .* regFacs; % Regularisation matrix
+        regMat = eye(size(Smm)) .* regFacs;
     else
         regMat = diag(regFacs);
     end
 
+    % Signal-to-Noise Ratio at the monitoring microphones
+    if ~isinf(snrVal)
+        snrVal = 10^(-snrVal/10); % Convert deciBel to linear
+        snrMtx = (snrVal.^2) .* norm(Smm, 'fro')/(size(Pm, 1)^2); % Calculate the appropriate SNR amplitude values
+    else
+        snrMtx = 1;
+    end
+    snrMtx = snrMtx .* eye(size(Pm, 1));
+
     % Calcualte observation filters
-    invQty = Smm + regMat;
+    invQty = Smm + snrMtx + regMat;
     oOpt = Sme/invQty;
 
     % Condition number of auto-correlation (power spectrum) matrix