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Commit f495bca5 authored by Achilles Kappis's avatar Achilles Kappis
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Fixed errors in header documentation, fixed calculations of mean O to be... 

Fixed errors in header documentation, fixed calculations of mean O to be calculated by averaged Rme and Rmm and introduced separate (fractional) delays for each virtual microphone in either samples or seconds
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Virtual Sensing/Remote Microphone Technique/MATLAB/Functions/obsFiltTD.m
+ 71
− 35
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......@@ -3,7 +3,7 @@
% Author: Achilles Kappis
% e-mail: axilleaz@protonmail.com
%
% Date: 12/09/2024 (DD/MM/YYYY)
% Date: 13/09/2024 (DD/MM/YYYY)
%
% Copyright: MIT
% --------------------------------------------------
......@@ -37,14 +37,27 @@
% delay [numeric] (Optional): The delay to be added to the virtual
% microphone signals, effectively implementing
% the delayed Remote Microphone Technique. This
% must be a real non-negative scalar less than
% can be either a scalar, in which case the
% same delay is applied to all virtual
% microphone signals, or a vector with each
% element holding the delay for each virtual
% microphone signal respectively. The delays
% must be real non-negative values less than
% or equal to I, the length of the virtual
% microphone signals. [Default: 0].
% microphone signals. If "fs" is provided,
% these values correspond to seconds otherwise
% they are in samples and must be integer.
% [Default: 0].
%
% fs [numeric] (Optional): The sampling frequency. If this value is given,
% the delay is treated as being in seconds,
% otherwise it is in samples. This argument must
% be a positive real integer.
%
% --------------------------------------------------
% Output
%
% O [numeric]: The observation filters. This is an firLenxNxMxJ array.
% O [numeric]: The observation filters. This is an NxfirLenxMxJ array.
%
% Rme [numeric]: The cross-correlation matrices between the monitoring and
% virtual microphone signals. This is an filtLenxMxNxJ
......@@ -78,10 +91,13 @@
% can be used directly with either "Ovec" or "Oopt"
% arguments to perform the filtering/estimation.
%
% Omean [numeric]: The observation filters averaged over the trials/sound
% field realisations. This is an NxMxfirLen array.
% Omean [numeric]: The observation filters calculated with the correlation
% matrices RmeMtx and RmmMtx averaged over the
% trials/sound field realisations (these are the
% "RmeMtxMean" and "RmmMtxMean" output arguments). This is
% an NxfirLenxM array.
%
% Rmemean [numeric]: The cross-correlation matrices between the monitoring
% RmeMean [numeric]: The cross-correlation matrices between the monitoring
% and virtual microphone signals averaged over the
% trials/sound field realisations. This is an
% NxMxfirLen array.
......@@ -94,10 +110,11 @@
% Oopt [numeric]: This is a matrix with the observation filters
% stacked/vectorised, so that they can be applied to a
% stacked/vectorised monitoring microphone measurements
% vector, averaged over the trials/sound field
% realisations. The dimensions of this matrix are
% NxTotFiltLen. These filters are denoted "optimal" since
% they are averaged over many realisations (if J > 1).
% vector, calculated with the correlation matrices RmeMtx
% and RmmMtx averaged over all trials/sound field
% realisations (the "RmeMtxMean" and "RmmMtxMean" output
% arguments). The dimensions of this matrix are
% NxTotFiltLen.
%
% RmeMtxMean [numeric]: This is the matrix with the cross-correlation
% vectors between the monitoring and virtual
......@@ -119,11 +136,11 @@
% Stephen J. Elliott and Jordan Cheer.
%
% --------------------------------------------------
function [O, Rme, Rmm, Ovec, RmeMtx, RmmMtx, mMtx, Omean, RmeMean, RmmMean, Oopt, RmeMtxMean, RmmMtxMean] = obsFiltTD(e, m, beta, filtLen, delay)
function [O, Rme, Rmm, Ovec, RmeMtx, RmmMtx, mMtx, Omean, RmeMean, RmmMean, Oopt, RmeMtxMean, RmmMtxMean] = obsFiltTD(e, m, beta, filtLen, delay, fs)
% ====================================================
% Check for number of arguments
% ====================================================
narginchk(2, 5);
narginchk(2, 6);
nargoutchk(0, 13);
% ====================================================
......@@ -135,24 +152,50 @@ function [O, Rme, Rmm, Ovec, RmeMtx, RmmMtx, mMtx, Omean, RmeMean, RmmMean, Oopt
% Validate optional arguments
if nargin > 2 && ~isempty(beta)
validateattributes(beta, "numeric", {'scalar', 'nonnegative', 'nonnan', 'nonempty', 'finite'}, mfilename, "Regularisation factor", 3);
validateattributes(beta, "numeric", {'scalar', 'nonnegative', 'nonnan', 'nonempty', 'finite', 'real'}, mfilename, "Regularisation factor", 3);
else
beta = 0;
end
if nargin > 3 && ~isempty(filtLen)
validateattributes(filtLen, "numeric", {'scalar', 'positive', 'nonnan', 'nonempty', 'finite', '<=', size(m, 1)}, mfilename, "Length of observation filters", 4);
validateattributes(filtLen, "numeric", {'scalar', 'positive', 'nonnan', 'real', 'nonempty', 'finite', '<=', size(m, 1)}, mfilename, "Length of observation filters", 4);
else
filtLen = size(m, 2);
end
if nargin > 4 && ~isempty(delay)
validateattributes(delay, "numeric", {'scalar', 'nonnegative', 'nonnan', 'finite', 'nonempty', '<=', size(e, 1)}, mfilename, "Delay to be added to virtual microphone signals to implement the delayed Remote Microphone Technique", 5);
validateattributes(delay, "numeric", {'vector', 'nonnegative', 'nonnan', 'finite', 'real', 'nonempty', '<=', size(e, 1)}, mfilename, "Delay to be added to virtual microphone signals to implement the delayed Remote Microphone Technique", 5);
if isscalar(delay)
delay = delay * ones(size(e, 2), 1);
elseif length(delay) ~= size(e, 2)
error("The 'delay' argument must be either a scalar, or a vector with number of elements equal to the number of virtual microphone signals.");
end
else
delay = 0;
delay = zeros(size(e, 2), 1);
end
if nargin > 5 && ~isempty(fs)
validateattributes(fs, "numeric", {'scalar', 'integer', 'positive', 'nonnan', 'nonempty', 'finite', 'real'}, mfilename, "The sampling frequency", 6);
else
fs = [];
end
% ====================================================
% Pre-process data
% ====================================================
% Delay the virtual microphone signals
if isempty(fs)
for jIdx = size(e, 3):-1:1
e(:, :, jIdx) = delayseq(e(:, :, jIdx), delay);
end
else
for jIdx = size(e, 3):-1:1
e(:, :, jIdx) = delayseq(e(:, :, jIdx), delay, fs);
end
end
% ====================================================
% Calculate cross- and auto-correlation matrices
% ====================================================
......@@ -165,7 +208,7 @@ function [O, Rme, Rmm, Ovec, RmeMtx, RmmMtx, mMtx, Omean, RmeMean, RmmMean, Oopt
[corr, lags] = xcorr(m(:, mIdx, jIdx), e(:, eIdx, jIdx));
lIdx = find(lags == 0);
Rme(:, mIdx, eIdx, jIdx) = corr(delay + lIdx:-1:lIdx-filtLen + 1);
Rme(:, mIdx, eIdx, jIdx) = corr(lIdx:-1:lIdx-filtLen + 1);
end
% Go through the monitoring microphones to calculate the monitoring microphone correlation matrices
......@@ -215,9 +258,17 @@ function [O, Rme, Rmm, Ovec, RmeMtx, RmmMtx, mMtx, Omean, RmeMean, RmmMean, Oopt
mMtx = reshape(m, prod(size(m, [1, 2])), size(m, 3));
end
% Mean observation over trials/sound field realisations
% Observation filter calculated with the mean Rme and Rmm over the trials/sound field realisation
if nargout > 7
Omean = mean(O, 4);
% Average the RmeMtx and RmmMtx matrices
RmeMtxMean = mean(RmeMtx, 3);
RmmMtxMean = mean(RmmMtx, 3);
% Calculate the observation filter
Oopt = RmeMtxMean.'/(RmmMtxMean + beta * eye(size(RmmMtxMean)));
% Reshape
Omean = permute(reshape(Oopt.', filtLen, size(m, 2), size(e, 2)), [3, 1, 2]);
end
% Mean cross-correlations between monitoring and virtual microphones over trials/sound field realisations
......@@ -229,19 +280,4 @@ function [O, Rme, Rmm, Ovec, RmeMtx, RmmMtx, mMtx, Omean, RmeMean, RmmMean, Oopt
if nargout > 9
RmmMean = mean(Rmm, 5);
end
% Average of optimal observation filters over trials/sound field realisations
if nargout > 10
Oopt = mean(Ovec, 3);
end
% Mean cross-correlation matrix between monitoring and virtual microphones over trials/sound field realisations
if nargout > 11
RmeMtxMean = mean(RmeMtx, 3);
end
% Mean cross-correlation matrix of monitoring microphones over trials/sound field realisations
if nargout > 12
RmmMtxMean = mean(RmmMtx, 3);
end
end
\ No newline at end of file
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