Continuous Sound and Vibration Analysis: [s]=calc_diff_metrics_cont(s, diff_chan, flag, ratio_metrics, round_kind, round_digits, abs_other) - File Exchange

Code covered by the BSD License

Highlights from
Continuous Sound and Vibration Analysis

choosebox(varargin) CHOOSEBOX Two-listed item selection dialog box.
selectdlg2( cellItems, guiTit... selectdlg2 Generate a scrolled matrix of choices for user selection.
tableGUI(varargin) TABLEGUI - Spreadsheet like display and edition of a generic 2D array. By generic it is
ACdsgn(Fs)
ArgStruct=parseArgs(args,ArgS... Helper function for parsing varargin.
[ ha_chan2, ha_axes2, ha_acce... % config_ha_accels: Configures the axis directions and channels for accelerometers measuring hand arm vibrations
[ wb_chan2, wb_axes2, wb_acce... % config_wb_accels: Configures the axis directions and channels for accelerometers measuring whole body vibrations
[A2, A_str, real_digitsL, rea... % m_round: Rounds an array to a specified number of significant digits or a specified digits place: significant figures, sigfigs
[A2, A_str, real_digitsL, rea... % pow10_round: Round a numeric array to a Specified Digits Place
[A2, A_str, real_digitsL, rea... % sd_round: Rounds an array to a specified number of Significant Digits, significant figures, digits of precision
[Ad]=filter_attenuation(f, fc... % filter_attenuation: Nth octave band filter attenuation
[B, A]=Nth_octdsgn(Fs, Fc, N,...
[B, A]=hand_arm_fil(Fs) % hand_arm_fil: Calculates the hand arm vibrations filter coefficients
[Fsn, p, q, errors]=get_p_q2(...
[LeqA, LeqA8, LeqC, LeqC8, Le... % Leq_all_calc: Calculates levels and peaks for A, C, and linear weighting
[SP, vibs, Fs_SP, Fs_vibs, de... % data_loader2: Loads data specified as sound or vibrations and further specified as data, time increment or sampling rate
[SP2, mean_array2, mean1, arr... % sub_mean: Removes the running average from a time record given a sampling rate and high pass cutoff frequency.
[SP2, mean_array2]=sub_mean(S... % sub_mean: Removes the running average from a time record given a sampling rate and high pass cutoff frequency.
[alpha]=t_alpha(t, nu) % t_alpha: cumulative probability function of the t-distribution,
[arms_w, arms8h_w, Dy_w, Dy_5... % Vibs_calc_hand_arm: Calculates the metrics for the hand arm vibrations
[arms_wb, arms8h_wb, VDV_wb, ... % Vibs_calc_whole_body: Calculates the metrics for the whole body vibrations
[buf2, num_files_not_empty]=s... % splat_cell: Returns a row vector of numbers containng all of the numbers in a cell array
[bz, az]=bessel_digital(Fs, F... % bessel_digital: creates a digital low pass bessel filter of order n
[comb_data_buf2]=combine_acce... % combine_accel_directions_ha: Calculates overall hand arm vibrations acceleration metrics from multiple axis accelerometers.
[comb_data_buf2]=combine_acce... % combine_accel_directions_wb: Calculates overall whole body vibrations acceleration metrics from multiple axis accelerometers.
[fc_out, SP_levels, SP_peak_l... % Nth_oct_time_filter2: Calculates the Nth octave center frequencies, sound levels, peak levels, and time records
[fc_out, SP_levels, SP_peak_l... % fourier_nth_oct_time_filter: Computes fraction octave filters using ffts or hilbert transforms.
[fid, mean_vals, max_num_chan... % print_channel_stats: Print the ststistics for each channel for the impulsive sound table
[fid]=print_overall_stats(fid... % print_overall_stats: Print the overall statistics for the impulsive sound table
[filename_base, ext]=file_ext... % file_extension: separates a filename and path from the file extension
[filtercoeffs, filter_types, ... % whole_Body_Filter: Calculates the filter coefficients for whole body vibrations.
[h, h2, wb_th_array]=plot_snd... % plot_snd_vibs(: plots sound and vibrations data in the time domain
[interval, error]=t_confidenc... % t_confidence_interval: One or two sided confidence interval of standard error with t distribution
[m2]=geospace(a, b, n, flag) % geospace: caculates a geometric sequence or psuedogeometric sequence from a to b with n elements
[ndraw, count, count2, error]... % rand_int: Quckly generates n random integers from a to b integer
[num_accels, accel_num_chan, ... % accel_config: Configures the accelerometer channels given the cell array axes_config
[num_accels, accel_num_chan, ... % config_accels: Configures the accelerometer channels using an input dialog box.
[num_channels_a, num_diff_cha... % table_append_channels: caculates the number of channels of data for making a table
[num_samples_ca, s]=num_impul... % num_impulsive_samples: Counts the number of impulsive noise samples
[nums, matches]=find_nums(str... % find_nums: Finds floating point complex, real, integer, and currency (dollars) numbers in a string
[out]=print_data_loader_confi... % print_data_loader_configuration_table: Prints the variable configuration to a table
[pfq]=genHyper(a,b,z,lnpfq,ix... function [pfq]=genHyper(a,b,z,lnpfq,ix,nsigfig)
[prms]=rms_val(p, dim) % rms_val: Calculates the rms value along a specific dimension
[ptsa, nptsa, rt_stats, other... % data_outliers: Determine the outliers and return descriptive statistics
[res,raw]=fastmcd(data,option... version 22/12/2000, revised 19/01/2001,
[s]=calc_diff_metrics_cont(s,... % calc_diff_metrics_cont: Calculates the differences in metrics between pairs of channels: Impulsive Noise Meter
[snd, vibras_ha, vibras_wb, v... % Continuous_Sound_and_Vibrations_Analysis: Loads time records and timeseries, Calculates metrics for sound and vibrations (hand arm and whole body).
[t_SP_rs, SP_rs]=resample_plo... % resample_plot: Resamples a plot to 10000 data points using the max and%min from each bin
[t_out, T_out, error1_out, er... % inverse cumulative probability function, t-distribution
[varargout]=convert_double(va... % This program converts the inputs into double precision arrays. Then
[y, x, a]=match_height_and_sl... % match_height_and_slopes2: creates a quartic with specifed height and slope at the end points.
[y2, num_settle_pts, settling... % filter_settling_data: Creates data to append to a time record for settling a filter
[y2]=remove_filter_settling_d... % remove_filter_settling_data: removes data added to time records to settle the filter
[yAC, errors]=ACweight_time_f... % ACweight_time_filter: Applies an A or C weighting time filter to a sound recording
[y]=moving(x,m,fun) MOVING will compute moving averages of order n (best taken as odd)
[y_out, b, a]=bessel_antialia... % bessel_antialias: applies an antialiasing digital Bessel filter
[y_out, t_out, b, a]=bessel_d... % bessel_down_sample: applies an antialiasing digital Bessel filter
[y_out, x_out, y_in]=resample... % resample_interp3: resamples using interp1 with additional options
[yh, B, A, errors]=hand_arm_t... % hand_arm_time_fil: Calculates the hand arm vibrations filter coefficients and returns the filtered time record
[yhw, filtercoeffs, filter_ty... % whole_body_time_filter: Calculates the whole body vibrations filter coefficients and returns the filtered time record
[ytick_m, YTickLabel1, ytick_... % fix_YTick: Sets appropriate Y-Tick values for small plots
channel_data_type_selection(n... % channel_data_type_selection
fastlts(x,y,options) version 22/12/2000, revised 19/01/2001, 30/01/2003
h=subaxis(varargin) SUBAXIS Create axes in tiled positions. (just like subplot)
kurtosis2(x, dimension) % This program calculates the kurtosis.
main_sound_and_vibs % Main_snd_and_vibs: Main Program for continuous sound and vibrations analysis
make_summary_cont_stats_table... % make_summary_cont_stats_table: Makes a table of the output of the
nth_freq_band(N, min_f, max_f... % nth_freq_band: Calculates the 1/nth octave frequency bands center, lower, and upper bandedge limits
psuedo_box(h_array)
rmean(y, db_or_lin) % get_stats: Calculates descriptive statistics for the input variable y.
save_a_plot2_audiological(a, ... % save_a_plot2_audiological: Saves current figure to specified image type.
variable_data_type_selection(... % channel_data_type_selection
View all files

from Continuous Sound and Vibration Analysis by Edward Zechmann
This program analyzes sound and vibrations data using metrics for continuous noise and vibrations.

[s]=calc_diff_metrics_cont(s, diff_chan, flag, ratio_metrics, round_kind, round_digits, abs_other)

function [s]=calc_diff_metrics_cont(s, diff_chan, flag, ratio_metrics, round_kind, round_digits, abs_other)
% % calc_diff_metrics_cont: Calculates the differences in metrics between pairs of channels: Impulsive Noise Meter
% %
% % Syntax:
% %
% % [s]=calc_diff_metrics_cont(s, diff_chan, flag)
% %
% % *****************************************************************
% %
% % Description:
% %
% % This program takes the output from the Impulsive_Noise_Meter and
% % calculates the differences in metrics between pairs of channels:
% % Impulsive Noise Meter.
% %
% % This program is specifically for analyzing hearing protection for
% % impulsive noise.
% %
% % *****************************************************************
% %
% % Input Variables
% %
% % s is the data structure created using the Impulsive_Noise_Meter.
% %
% % diff_chan=[1,2];    % A paired column vector of channel numbers.
% %                     %
% %                     % The odd and even elements of the vector are
% %                     % paired for calculating differences
% %                     % in metrics across channels.
% %                     %
% %                     % default is diff_chan=(1:2)';
% % 
% % flag=1;             % flag selects the data type to be analyzed
% %                     % 1 is for Sound data
% %                     % 2 is for Hand Arm 
% %                     % 3 is for Whole Body
% %                     % 4 is for Motion Sicknes
% %                     % 
% %                     % The default value is 1
% % 
% % ratio_metrics=[3, 4, 5, 20];    % is an array of indices of metrics for
% %                                 % the diff_chans array that are
% %                                 % calculated as ratios instead of
% %                                 % differences
% % 
% % round_kind=1;           % Array of values one element for the rta array
% %                         % and one element for each varargin array
% %                         % (see example)
% %                         % 1 round to specified number of significant
% %                         % digits
% %                         %
% %                         % 0 round to specified digits place
% %                         %
% %                         % default is round_kind=1;
% %
% % round_digits=3;         % Array of values one element for the rta array
% %                         % and one element for each varargin array
% %                         % (see example)% Type of rounding depends on round_kind
% %                         %
% %                         % if round_kind==1 number of significant digits
% %                         % if round_kind==0 specified digits place
% %                         %
% %                         % default is round_digits=3;
% % 
% % *****************************************************************
% %
% % Output Variables
% %
% % s is the data structure created using the
% %      Continuous_Sound_and_Vibrations_Analysis program.
% %      The data type of s is determined by the input parameter flag.  
% %      
% % This program modifies s by adding the following variables
% %      diff_chan
% %      diff_metrics
% %      diff_stats_of_metrics
% %     
% % *****************************************************************
% %
% Example='s';
%
% % This is an example using shock tube data! The data compares two
% % data acquisition rates.
%
% % An example which outputs the mean of the metrics.
%
% load shock_tube_cont;
% stat_to_get=1;
% fileout='analysis1';
% [bb_table, bb2]=make_table_compare_systems_cont(s, stat_to_get, fileout);
%
%
% % An example which outputs all of the metrics.
%
% load shock_tube_cont;
% stat_to_get=[1:8];
% fileout='analysis2';
% [bb_table, bb2]=make_table_compare_systems_cont(s, stat_to_get, fileout);
% 
% 
% 
% % ********************************************************************
% %
% %
% % Subprograms
% %
% %
% % 
% % List of Dependent Subprograms for 
% % calc_diff_metrics_cont
% % 
% % FEX ID# is the File ID on the Matlab Central File Exchange
% % 
% % 
% % Program Name   Author   FEX ID#
% %  1) fastlts		Peter J. Rousseeuw		NA	
% %  2) fastmcd		Peter J. Rousseeuw		NA	
% %  3) genHyper		Ben Barrowes		6218	
% %  4) m_round		Edward L. Zechmann			
% %  5) pow10_round		Edward L. Zechmann			
% %  6) rmean		Edward L. Zechmann			
% %  7) sd_round		Edward L. Zechmann			
% %  8) t_alpha		Edward L. Zechmann			
% %  9) t_confidence_interval		Edward L. Zechmann			
% % 10) t_icpbf		Edward L. Zechmann			
% %
% %
% % *****************************************************************
% %
% % Written by Edward L. Zechmann
% %
% %     date   1 November   2008
% %
% % modified 15 January     2009    Updated the descriptive statistics to 
% %                                 include the arthimetic mean.  
% %
% % modified 18 January     2009    Updated to include rounding. 
% %
% % modified  9 October     2009    Updated Comments
% % 
% % modified  5 Janaury     2012    Replace LMSloc with fastlts.  
% %                                 Updated comments
% %
% %
% %
% % *****************************************************************
% %
% % Please feel free to modify this code.
% %
% % See Also:  calc_diff_metrics, make_table_compare_systems, Impulsive_Noise_Meter, Continuous_Sound_and_Vibrations_Analysis
% %


if nargin < 1 || isempty(s) || ~iscell(s)
    load('shock_tube_cont');
    warning('Loading and analyzing the default shock tube data.');
end

if nargin < 2 || isempty(diff_chan) || ~isnumeric(diff_chan)
    diff_chan=(1:2)';
end

if nargin < 3 || isempty(flag) || ~isnumeric(flag)
    flag=1;
end

flag=round(flag);
if flag < 1
    flag=1;
end

if flag > 4 
    flag=4;
end

if nargin < 4 || isempty(ratio_metrics) || ~isnumeric(ratio_metrics)
    ratio_metrics=[3, 4, 5, 20];
end

if nargin < 5 || isempty(round_kind) || ~isnumeric(round_kind)
    round_kind=1;
end


if nargin < 6 || isempty(round_digits) || ~isnumeric(round_digits)
    round_digits=3;
end




num_files=0;
num_vars=0;
num_accels=0;
num_postures=0;

switch flag

    case 1
        % snd
        % Sound
        [num_files, num_vars]=size(s);
        num_channels_array=zeros(num_files,1);
    case 2
        % vibras_ha
        % Hand Arm Vibrations
        [num_files, num_vars, num_accels]=size(s);
        num_channels_array=zeros(num_files,1);
    case 3
        % vibras_wb
        % Whole Body Vibrations
        [num_files, num_vars, num_accels, num_postures]=size(s);
        num_channels_array=zeros(num_files,1);
    case 4
        % vibras_ms
        % Motion Sickness
        [num_files, num_vars, num_accels]=size(s);
        num_channels_array=zeros(num_files,1);
    otherwise
        % snd
        % Sound
        [num_files, num_vars]=size(s);
        num_channels_array=zeros(num_files,1);
end


% Determine the size of the concatenated metrics table
for e1=1:num_files;    % Data files

    num_channels=[];

    for e2=1:num_vars; % Number of Variables (Number of Data Acquisition Systems

        switch flag

            case 1
                if ~isempty(s{e1,e3})
                    [num_metrics, num_channels, num_stats2]=size(s{e1,e2}.stats_of_metrics);
                    if num_channels >= 1
                        num_channels_array(e1)=max([num_channels, num_channels_array(e1)]);
                    end
                end
            case 2
                for e3=1:num_accels;
                    if ~isempty(s{e1,e3})
                        [num_metrics, num_channels, num_stats2]=size(s{e1,e2,e3}.stats_of_total_metrics);
                        if num_channels >= 1
                            num_channels_array(e1)=max([num_channels, num_channels_array(e1)]);
                        end
                    end
                end
            case 3
                for e3=1:num_accels;
                    for e4=1:num_postures;
                        if ~isempty(s{e1,e3})
                            [num_metrics, num_channels, num_stats2]=size(s{e1,e2,e3,e4}.stats_of_total_metrics);
                            if num_channels >= 1
                                num_channels_array(e1)=max([num_channels, num_channels_array(e1)]);
                            end
                        end
                    end
                end
            case 4
                for e3=1:num_accels;
                    if ~isempty(s{e1,e3})
                        [num_metrics, num_channels, num_stats2]=size(s{e1,e2,e3}.stats_of_total_metrics);
                        if num_channels >= 1
                            num_channels_array(e1)=max([num_channels, num_channels_array(e1)]);
                        end
                    end
                end
            otherwise
                if ~isempty(s{e1,e3})
                    [num_metrics, num_channels, num_stats2]=size(s{e1,e2}.stats_of_metrics);
                    if num_channels >= 1
                        num_channels_array(e1)=max([num_channels, num_channels_array(e1)]);
                    end
                end

        end

    end

end



num_kinds=length(round_kind);
num_digits=length(round_digits);


num_diff_chan=floor(length(diff_chan)/2);

num_accels2=max([num_accels,1]);
num_postures2=max([num_postures,1]);

% Fill in the concatenated metrics table
% Add row headings and column headings

for e1=1:num_files;    % Data files

    for e2=1:num_vars; % Number of Variables (Number of Data Acquisition Systems)

        for e3=1:num_accels2;

            for e4=1:num_postures2;


                switch flag
                    case 1
                        [num_metrics, num_channels, num_stats2]=size(s{e1,e2}.stats_of_metrics);
                    case 2
                        [num_metrics, num_channels, num_stats2]=size(s{e1,e2,e3}.stats_of_total_metrics);
                    case 3
                        [num_metrics, num_channels, num_stats2]=size(s{e1,e2,e3,e4}.stats_of_total_metrics);
                    case 4
                        [num_metrics, num_channels, num_stats2]=size(s{e1,e2,e3}.stats_of_total_metrics);
                    otherwise
                        [num_metrics, num_channels, num_stats2]=size(s{e1,e2}.stats_of_metrics);
                end

                if num_channels >= 1

                    switch flag
                        case 1
                            s{e1,e2}.diff_chan=diff_chan;
                        case 2
                            s{e1,e2,e3}.diff_chan=diff_chan;
                        case 3
                            s{e1,e2,e3,e4}.diff_chan=diff_chan;
                        case 4
                            s{e1,e2,e3}.diff_chan=diff_chan;
                        otherwise
                            s{e1,e2}.diff_chan=diff_chan;
                    end

                    % calculate the differences, then the statistics then
                    % save them to the structure s.
                    for e5=1:num_diff_chan; % Channels

                        for e6=1:num_metrics; % Data Metrics

                            switch flag
                                case 1
                                    buf1=s{e1,e2}.metrics{diff_chan(2*e5-1), 1}(:, e6);
                                    buf2=s{e1,e2}.metrics{diff_chan(2*e5), 1}(:, e6);
                                case 2
                                    buf1=s{e1,e2,e3}.total_metrics{diff_chan(2*e5-1), 1}(:, e6);
                                    buf2=s{e1,e2,e3}.total_metrics{diff_chan(2*e5), 1}(:, e6);
                                case 3
                                    buf1=s{e1,e2,e3,e4}.total_metrics{diff_chan(2*e5-1), 1}(:, e6);
                                    buf2=s{e1,e2,e3,e4}.total_metrics{diff_chan(2*e5), 1}(:, e6);
                                case 4
                                    buf1=s{e1,e2,e3}.total_metrics{diff_chan(2*e5-1), 1}(:, e6);
                                    buf2=s{e1,e2,e3}.total_metrics{diff_chan(2*e5), 1}(:, e6);
                                otherwise
                                    buf1=s{e1,e2}.metrics{diff_chan(2*e5-1), 1}(:, e6);
                                    buf2=s{e1,e2}.metrics{diff_chan(2*e5), 1}(:, e6);
                            end
                            
                            [m1_buf1, n1_buf1]=size(buf1);
                            [m1_buf2, n1_buf2]=size(buf2);

                            m1_buf3=min([m1_buf1 m1_buf2]);
                            n1_buf3=min([n1_buf1 n1_buf2]);

                            
                            
                            if num_kinds >= (e6)
                                rk=round_kind(e6);
                            else
                                rk=1;
                            end


                            if num_digits >= round_digits(e6)
                                rd=round_digits(e6);
                            else
                                rd=3;
                            end
                            
                            % Determine whether to compute a difference or
                            % ratio.  Calculate a ratio for metrics  that
                            % are a member of ratio_metrics.
                            if ismember(e6, ratio_metrics) 
                                rk=1;
                                rd=3;
                                buf3=abs(buf1(1:m1_buf3,1:n1_buf3))./abs(buf2(1:m1_buf3,1:n1_buf3));
                            else
                                buf3=abs(buf1(1:m1_buf3,1:n1_buf3))-abs(buf2(1:m1_buf3,1:n1_buf3));
                            end
                            

                            
                            switch flag
                                case 1
                                    s{e1,e2}.diff_metrics{e5,1}(:,e6)=m_round(buf3, rk, rd);
                                case 2
                                    s{e1,e2,e3}.diff_total_metrics{e5,1}(:,e6)=m_round(buf3, rk, rd);
                                case 3
                                    s{e1,e2,e3,e4}.diff_total_metrics{e5,1}(:,e6)=m_round(buf3, rk, rd);
                                case 4
                                    s{e1,e2,e3}.diff_total_metrics{e5,1}(:,e6)=m_round(buf3, rk, rd);
                                otherwise
                                    s{e1,e2}.diff_total_metrics{e5,1}(:,e6)=m_round(buf3, rk, rd);
                            end
                            
                            % Calculate the Arithmetic Mean
                            bmean_avg1=mean(buf3);
                            
                            % Calculate the Robust Mean
                            [ bmean_avg2 ] = rmean(buf3(:), 0);
                            %bmean_avg2=LMSloc(buf3);

                            % Calculate standard deviation
                            stdrt=std(buf3);             

                            % Calculate 95% confidence interval of
                            [ci_int]=t_confidence_interval(buf3, 0.95); 

                            % the standard error of the
                            % t-distribution with a two-sided test.
                            medianrt=median(buf3);
                            
                            % Calculate teh MEdian Index
                            [mbuf ix]=min(abs(buf3-medianrt));
                            
                            % Calculate the Minimum
                            minrt=min(buf3);
                            
                            % Calculate the Maximum
                            maxrt=max(buf3);

                            
                            switch flag
                                case 1
                                    s{e1,e2}.diff_stats_of_metrics(e6, e5, 1)=m_round(bmean_avg1, rk, rd);
                                    s{e1,e2}.diff_stats_of_metrics(e6, e5, 2)=m_round(bmean_avg2, rk, rd);
                                    s{e1,e2}.diff_stats_of_metrics(e6, e5, 3)=m_round(stdrt, 1, 3);
                                    s{e1,e2}.diff_stats_of_metrics(e6, e5, 4)=m_round(ci_int, 1, 3);
                                    s{e1,e2}.diff_stats_of_metrics(e6, e5, 5)=ix;
                                    s{e1,e2}.diff_stats_of_metrics(e6, e5, 6)=m_round(medianrt, rk, rd);
                                    s{e1,e2}.diff_stats_of_metrics(e6, e5, 7)=m_round(minrt, rk, rd);
                                    s{e1,e2}.diff_stats_of_metrics(e6, e5, 8)=m_round(maxrt, rk, rd);
                                case 2
                                    s{e1,e2,e3}.diff_stats_of_total_metrics(e6, e5, 1)=m_round(bmean_avg1, rk, rd);
                                    s{e1,e2,e3}.diff_stats_of_total_metrics(e6, e5, 2)=m_round(bmean_avg2, rk, rd);
                                    s{e1,e2,e3}.diff_stats_of_total_metrics(e6, e5, 3)=m_round(stdrt, rk, rd);
                                    s{e1,e2,e3}.diff_stats_of_total_metrics(e6, e5, 4)=m_round(ci_int, 1, 3);
                                    s{e1,e2,e3}.diff_stats_of_total_metrics(e6, e5, 5)=ix;
                                    s{e1,e2,e3}.diff_stats_of_total_metrics(e6, e5, 6)=m_round(medianrt, rk, rd);
                                    s{e1,e2,e3}.diff_stats_of_total_metrics(e6, e5, 7)=m_round(minrt, rk, rd);
                                    s{e1,e2,e3}.diff_stats_of_total_metrics(e6, e5, 8)=m_round(maxrt, rk, rd);
                                case 3
                                    s{e1,e2,e3,e4}.diff_stats_of_total_metrics(e6, e5, 1)=m_round(bmean_avg1, rk, rd);
                                    s{e1,e2,e3,e4}.diff_stats_of_total_metrics(e6, e5, 2)=m_round(bmean_avg2, rk, rd);
                                    s{e1,e2,e3,e4}.diff_stats_of_total_metrics(e6, e5, 3)=m_round(stdrt, rk, rd);
                                    s{e1,e2,e3,e4}.diff_stats_of_total_metrics(e6, e5, 4)=m_round(ci_int, 1, 3);
                                    s{e1,e2,e3,e4}.diff_stats_of_total_metrics(e6, e5, 5)=ix;
                                    s{e1,e2,e3,e4}.diff_stats_of_total_metrics(e6, e5, 6)=m_round(medianrt, rk, rd);
                                    s{e1,e2,e3,e4}.diff_stats_of_total_metrics(e6, e5, 7)=m_round(minrt, rk, rd);
                                    s{e1,e2,e3,e4}.diff_stats_of_total_metrics(e6, e5, 8)=m_round(maxrt, rk, rd);
                                case 4
                                    s{e1,e2,e3}.diff_stats_of_total_metrics(e6, e5, 1)=m_round(bmean_avg1, rk, rd);
                                    s{e1,e2,e3}.diff_stats_of_total_metrics(e6, e5, 2)=m_round(bmean_avg2, rk, rd);
                                    s{e1,e2,e3}.diff_stats_of_total_metrics(e6, e5, 3)=m_round(stdrt, rk, rd);
                                    s{e1,e2,e3}.diff_stats_of_total_metrics(e6, e5, 4)=m_round(ci_int, 1, 3);
                                    s{e1,e2,e3}.diff_stats_of_total_metrics(e6, e5, 5)=ix;
                                    s{e1,e2,e3}.diff_stats_of_total_metrics(e6, e5, 6)=m_round(medianrt, rk, rd);
                                    s{e1,e2,e3}.diff_stats_of_total_metrics(e6, e5, 7)=m_round(minrt, rk, rd);
                                    s{e1,e2,e3}.diff_stats_of_total_metrics(e6, e5, 8)=m_round(maxrt, rk, rd);
                                otherwise
                                    s{e1,e2}.diff_stats_of_metrics(e6, e5, 1)=m_round(bmean_avg1, rk, rd);
                                    s{e1,e2}.diff_stats_of_metrics(e6, e5, 2)=m_round(bmean_avg2, rk, rd);
                                    s{e1,e2}.diff_stats_of_metrics(e6, e5, 3)=m_round(stdrt, rk, rd);
                                    s{e1,e2}.diff_stats_of_metrics(e6, e5, 4)=m_round(ci_int, 1, 3);
                                    s{e1,e2}.diff_stats_of_metrics(e6, e5, 5)=ix;
                                    s{e1,e2}.diff_stats_of_metrics(e6, e5, 6)=m_round(medianrt, rk, rd);
                                    s{e1,e2}.diff_stats_of_metrics(e6, e5, 7)=m_round(minrt, rk, rd);
                                    s{e1,e2}.diff_stats_of_metrics(e6, e5, 8)=m_round(maxrt, rk, rd);
                            end
                            
                        end
                    end
                end
            end
        end
    end
end

Highlights from Continuous Sound and Vibration Analysis

Highlights from
Continuous Sound and Vibration Analysis