Continuous Sound and Vibration Analysis: [arms_w, arms8h_w, Dy_w, Dy_50_w, armf4_w, armf4_8h_w, Dy4_w, Dy4_50_w, peak_a_w, crest_factor_w, akurtosis_w, arms_un, arms8h_un, Dyun, Dyun_50, armf4_un, armf4_8h_un, Dy4_un, Dy4_un_50, peak_a_un, crest_factor_un, akurtosis_un, vibs_metrics_description, - 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.

[arms_w, arms8h_w, Dy_w, Dy_50_w, armf4_w, armf4_8h_w, Dy4_w, Dy4_50_w, peak_a_w, crest_factor_w, akurtosis_w, arms_un, arms8h_un, Dyun, Dyun_50, armf4_un, armf4_8h_un, Dy4_un, Dy4_un_50, peak_a_un, crest_factor_un, akurtosis_un, vibs_metrics_description,

function [arms_w, arms8h_w, Dy_w, Dy_50_w, armf4_w, armf4_8h_w, Dy4_w, Dy4_50_w, peak_a_w, crest_factor_w, akurtosis_w, arms_un, arms8h_un, Dyun, Dyun_50, armf4_un, armf4_8h_un, Dy4_un, Dy4_un_50, peak_a_un, crest_factor_un, akurtosis_un, vibs_metrics_description, vibs_metrics_units]=Vibs_calc_hand_arm(vibs2hw_ha, vibs2, Fs)
% % Vibs_calc_hand_arm: Calculates the metrics for the hand arm vibrations
% % 
% % Syntax;
% % 
% % [awrms, awrms8h, Dy, Dy_50, awhr4, awhr4_8h, Dy4, Dy4_50, peak_a, ...
% % crest_factor, awhkurtosis, arms, arms8h, Dyun, Dyun_50, ahr4, ...
% % ahr4_8h, Dy4un, Dy4un_50, peak_aun, crest_factorun, akurtosis, ...
% % vibs_metrics_description, vibs_metrics_units ...
% % ]=Vibs_calc_hand_arm(vibs2hw_ha, vibs2, Fs);
% % 
% %
% % ********************************************************************
% %
% % Description
% % 
% % This program calculates the continuous and impulsive hand arm 
% % vibration metrics and outputs the numeric metric values, and a
% % description and units for each metric.  Currenlty there are no metrics 
% % for individual impulsive vibration peaks such as blunt trauma to the
% % hand.  
% % 
% % 
% % 
% % ********************************************************************
% %
% % Input Variables
% % 
% % vibs2hw_ha=randn(1, 10000); % (m/s^2) Hand-Arm weighted rms acceleration 
% % 
% % vibs2=randn(1, 10000);      % (m/s^2) Linear weighted rms acceleration 
% % 
% % Fs=5000;                    % (Hz) Sampling Rate          
% % 
% % ********************************************************************
% %
% % Output Variables
% % 
% % Hand-Arm Weighted Outputs
% % 
% % arms_w                      (m/s^2) Hand-Arm weighted rms acceleration 
% % 
% % arms8h_w                    (m/s^2) 8 hour rms acceleration 
% % 
% % Dy_w                        (Years) Time until 10% chance of finger
% %                                     blanching
% % 
% % Dy_50_w                     (Years) Time until 10% chance of finger
% %                                     blanching using a 50% rest exposure
% % 
% % armf4_w                     (m/s^2) rmf^4 acceleration 
% % 
% % armf4_8h_w                  (m/s^2) 8 hour rmf^4 acceleration 
% % 
% % Dy4_w                       (Years) Time until 10% chance of finger
% %                                     blanching
% % 
% % Dy4_50_w                    (Years) Time until 10% chance of finger
% %                                     blanching
% % 
% % peak_a_w                    (m/s^2) Maximum Acceleration Value 
% % 
% % crest_factor_w              (No Units) crest factor of acceleration 
% % 
% % akurtosis_w                 (No Units) kurtosis of acceleration 
% % 
% % 
% % 
% % UnWeighted Outputs 
% % 
% % arms_un                     (m/s^2) Linear weighted rms acceleration 
% % 
% % arms8h_un                   (m/s^2) 8 hour rms acceleration 
% % 
% % Dyun                        (Years) Time until 10% chance of finger
% %                                     blanching
% % 
% % Dyun_50                     (Years) Time until 10% chance of finger
% %                                     blanching using a 50% rest exposure
% % 
% % armf4_un                    (m/s^2) rmf^4 acceleration 
% % 
% % armf4_8h_un                 (m/s^2) 8 hour rmf^4 acceleration 
% % 
% % Dy4_un                      (Years) Time until 10% chance of finger
% %                                     blanching
% % 
% % Dy4_un_50                   (Years) Time until 10% chance of finger
% %                                     blanching using a 50% rest exposure
% % 
% % peak_a_un                   (m/s^2) Maximum Acceleration Value 
% % 
% % crest_factor_un             (No Units) crest factor of acceleration 
% % 
% % akurtosis_un                (No Units) kurtosis of acceleration 
% % 
% % vibs_metrics_description    Cell Array of description of the vibrations
% %                                     metrics
% % 
% % vibs_metrics_units          Cell Array of Units of the vibrations
% %                                     metrics
% % 
% % ********************************************************************
% % 
% % 
% % 
% % Subprograms
% % 
% % 
% % List of Dependent Subprograms for 
% % Vibs_calc_hand_arm
% % 
% % FEX ID# is the File ID on the Matlab Central File Exchange
% % 
% % 
% % Program Name   Author   FEX ID#
% % 1) kurtosis2		William Murphy			
% % 
% % 
% % 
% % ********************************************************************
% %
% % Program Written by Edward L. Zechmann
% % 
% %     date    July        2007
% % 
% % modified  3 September   2008
% %
% % modified  9 October     2009    Updated Comments
% %
% % 
% % ********************************************************************
% %
% % Please Feel Free to Modify This Program
% %
% % See Also: hand_arm_fil2 Vibs_calc_hand_arm
% %

if (nargin < 1 || isempty(vibs2hw_ha)) || ~isnumeric(vibs2hw_ha) 
    vibs2hw_ha=randn(1, 10000);
end

if (nargin < 2 || isempty(vibs2)) || ~isnumeric(vibs2) 
    vibs2=randn(1, 10000);  
end

if (nargin < 2 || isempty(Fs)) || ~isnumeric(Fs) 
    Fs=5000;
end

vibs_metrics_description=cell(1, 22);
vibs_metrics_units=cell(1, 22);

%vibs_metrics_description2={ 'arms_w',  'arms8h_w', 'Dy_w',    '50% Dy_w', 'armf_w^4', 'armf8h_w^4', 'Dy_w^4', '50% Dy_w^4', 'Peak Accel_w', 'Crest Factor_w', 'akurtosis_w', 'arms_un',  'arms8h_un', 'Dy_un',  '50% Dy_un', 'armf_un^4', 'armf8h_un^4', 'Dy_un^4', '50% Dy_un^4', 'Peak Accel_un', 'Crest Factor_un', 'akurtosis_un' };
%vibs_metrics_units2={ '(m/s^2)', '(m/s^2)',  '(Years)', '(Years)', '(m/s^2)',   '(m/s^2)',    'Years',  'Years',      '(m/s^2)',    'No Units',       'No Units',    '(m/s^2)',  '(m/s^2)',  '(Years)', '(Years)',  '(m/s^2)',   '(m/s^2)',     'Years',   'Years',       '(m/s^2)',    'No Units',        'No Units' };

aa=length(vibs2hw_ha);

% Continuous vibration Analysis

% ******************************************************************
% Weighted Metrics
arms_w=sum(vibs2hw_ha'.^2).^(1/2)./(aa.^(1/2));
vibs_metrics_description{1}='arms_w';
vibs_metrics_units{1}='(m/s^2)';

arms8h_w= arms_w*sqrt(aa/Fs/(8*3600));
vibs_metrics_description{2}='arms8h_w';
vibs_metrics_units{2}='(m/s^2)';

Dy_w=31.8*(arms8h_w)^(-1.06);
vibs_metrics_description{3}='Dy_w';
vibs_metrics_units{3}='(Years)';

Dy_50_w=31.8*(0.5*arms8h_w)^(-1.06);
vibs_metrics_description{4}='50% Dy_w';
vibs_metrics_units{4}='(Years)';

%Impulsive Weighted vibration analysis
armf4_w=(sum(vibs2hw_ha'.^4).^(1/4))/(aa.^(1/4));
vibs_metrics_description{5}='armf_w^4';
vibs_metrics_units{5}='(m/s^2)';

armf4_8h_w=armf4_w*((aa/Fs/(8*3600)).^(1/4));
vibs_metrics_description{6}='armf8h_w^4';
vibs_metrics_units{6}='(m/s^2)';

Dy4_w=31.8*(armf4_8h_w)^(-1.06);
vibs_metrics_description{7}='Dy_w^4';
vibs_metrics_units{7}='(Years)';

Dy4_50_w=31.8*(0.5*armf4_8h_w)^(-1.06);
vibs_metrics_description{8}='50% Dy_w^4';
vibs_metrics_units{8}='(Years)';

peak_a_w=max(abs(vibs2hw_ha'));
vibs_metrics_description{9}='Peak Accel_w';
vibs_metrics_units{9}='(m/s^2)';

crest_factor_w=peak_a_w/arms_w;
vibs_metrics_description{10}='Crest Factor_w';
vibs_metrics_units{10}='No Units';

akurtosis_w=kurtosis2(vibs2hw_ha, 2);
vibs_metrics_description{11}='akurtosis_w';
vibs_metrics_units{11}='No Units';

% ******************************************************************
% Unweighted Metrics
arms_un=sum(vibs2'.^2).^(1/2)/(aa.^(1/2));
vibs_metrics_description{12}='arms_un';
vibs_metrics_units{12}='(m/s^2)';

arms8h_un= arms_un*sqrt(aa/Fs/(8*3600));
vibs_metrics_description{13}='arms8h_un';
vibs_metrics_units{13}='(m/s^2)';

Dyun=31.8*(arms8h_un)^(-1.06);
vibs_metrics_description{14}='Dy_un';
vibs_metrics_units{14}='(Years)';

Dyun_50=31.8*(0.5*arms8h_un)^(-1.06);
vibs_metrics_description{15}='50% Dy_un';
vibs_metrics_units{15}='(Years)';

%Impulsive Unweighted vibration analysis
armf4_un=(sum(vibs2'.^4).^(1/4))/(aa.^(1/4));
vibs_metrics_description{16}='armf_un^4';
vibs_metrics_units{16}='(m/s^2)';

armf4_8h_un=armf4_un*((aa/Fs/(8*3600)).^(1/4));
vibs_metrics_description{17}='armf8h_un^4';
vibs_metrics_units{17}='(m/s^2)';

Dy4_un=31.8*(armf4_8h_un)^(-1.06);
vibs_metrics_description{18}='Dy_un^4';
vibs_metrics_units{18}='(Years)';

Dy4_un_50=31.8*(0.5*armf4_8h_un)^(-1.06);
vibs_metrics_description{19}='50% Dy_un^4';
vibs_metrics_units{19}='(Years)';

peak_a_un=max(abs(vibs2'));
vibs_metrics_description{20}='Peak Accel_un';
vibs_metrics_units{20}='(m/s^2)';

crest_factor_un=peak_a_un/arms_un;
vibs_metrics_description{21}='Crest Factor_un';
vibs_metrics_units{21}='No Units';

akurtosis_un=kurtosis2(vibs2, 2);
vibs_metrics_description{22}='akurtosis_un';
vibs_metrics_units{22}='No Units';

Highlights from Continuous Sound and Vibration Analysis

Highlights from
Continuous Sound and Vibration Analysis