| [s, round_kind, round_digits]=Impulsive_Noise_Meter(filenamesin, fileout_txt, fileout_struct, save_struct, peak_identification_time, peak_metric_time, make_plot, save_plot, dB_scale, fig_format, Tool_Name, same_ylim, min_peak, ignore_threshold, plot_str, |
function [s, round_kind, round_digits]=Impulsive_Noise_Meter(filenamesin, fileout_txt, fileout_struct, save_struct, peak_identification_time, peak_metric_time, make_plot, save_plot, dB_scale, fig_format, Tool_Name, same_ylim, min_peak, ignore_threshold, plot_str, Align_peaks, portrait_landscape, peak_alignment_tol, sod, Polarized_or_ICP )
% % Impulsive_Noise_Meter: Loads sound time records, finds Peaks, calculates impulsive sound metrics
% %
% % Syntax;
% %
% % [s, round_kind, round_digits]=Impulsive_Noise_Meter(filenamesin, fileout_txt, fileout_struct,
% % save_struct, peak_identification_time, peak_metric_time, make_plot,
% % save_plot, dB_scale, fig_format, Tool_Name, same_ylim, min_peak, ignore_threshold, plot_str,
% % Align_peaks, portrait_landscape, peak_alignment_tol,
% % sod, Polarized_or_ICP );
% %
% %
% % ********************************************************************
% %
% % Description
% %
% % The main program is Main_Sound which has dialog boxes for selecting
% % filenames and other setting all of the paratmeters. For convenience
% % the best way to use Impulsive_Noise_Meter program is to type
% % "Main_Sound" at the comand line.
% %
% % The Impulsive_Noise_Meter program controls the data flow, and calls
% % other programs to plot the time records, identify the peaks, computes
% % metrics for impulsive noise.
% %
% % Then the program finds the impulsive peaks and identifies each peak
% % with a circle in each plot and numbers the odd peaks.
% %
% % Often several channels will be used to record the same impulsive noises
% % and it is useful to compare the same impulsive peaks. This program can
% % be set to select the corresponding peaks from all of the microphone
% % channels.
% %
% % The figures are saved to a file.
% %
% % The impulsive noise metrics are then calculated for each channel.
% % A statistical analysis is performed on the impulsive metrics and the
% % metrics and statistics are saved to a text file.
% %
% % Once all of the files have been processed and saved a copy of all the
% % data is saved to a matlab structure.
% %
% % Calculation of the MIL-STD_1474 B-duration assumes that the acoustic
% % time record has been corrected for the voltage inversion due to
% % polarization. 200 Volt polarized condenser microphones have voltage
% % inversion. ICP microphones do not have voltage inversion.
% %
% %
% % The input and output variables are described in the respective
% % sections below.
% %
% %
% %
% % ********************************************************************
% %
% % Input Variables
% %
% % filenamesin={'Impulse.mat'}; % cell array of filenames to process.
% % % default is uigetfile; a user windows
% % % interface for slecting files.
% %
% % fileout_txt='test.txt'; % Filename to save data to a tab
% % % delimited text file.
% %
% % fileout_struct='Mertics.mat';
% % % File name for the output Matlab Data
% % % structures file.
% % %
% % % default is 'data1_struct';
% %
% % save_struct=1; % Boolean controls whether to save all
% % % data to a matlab structure.
% % %
% % % default is save_struct=1;
% %
% % peak_identification_time=1; % 1 seconds. Time interval of data to
% % % identify each peak. Only one peak can
% % % exist in each interval of this time
% % % length.
% % %
% % % default is peak_identification_time=1;
% %
% % peak_metric_time=1; % 1 seconds. Time interval of data to
% % % analyze each peak. Amount of data for
% % % calculating metrics and Leqs.
% % %
% % % default is peak_metric_time=1;
% %
% % make_plot=1; % 1 to make plots for each channel
% % % analyzed. otherwise will not make any
% % % plots.
% %
% % save_plot=1; % 1 will save a plot.
% % % otherwise will not not save plots
% % % plots will not be saved if they are not
% % % made the file name of hte plot is the
% % % filename of the data file with the
% % % string '_peaks' appended at the end.
% %
% % dB_scale=0; % 1 use a dB scale to plot sound time record
% % % 0 use a linear scale to plot sound time record
% % %
% % % default is dB_scale=0; plot soudn tiem record in
% % % a linear scale such as Pascals.
% %
% % fig_format=[1,2,3,4,5,6]; % image format for saving the plot
% % % 1 pdf default is 1 suggested
% % % for simple documentation
% % % 2 fig
% % % 3 jpg (200 dpi resolution)
% % % 4 eps2
% % % 5 tiff (200 dpi resolution)
% % % 6 tiff (no compression) suggested for
% % % publications)
% % %
% % % default is fig_format=1; pdf format
% %
% % Tool_Name='Recip Saw'; % String which is the name of the tool
% % % or device under test.
% % % Typically should be 30 caracters or less.
% % %
% % % default is Tool_Name='';
% %
% % same_ylim=1; % 1 sets all of the limits of the y-axes
% % % to the same values for each channel.
% % % The y-limits of the sound data is
% % % independent of the y-limits of the
% % % vibrations data.
% % %
% % % if same_ylim ~= 1 then the y-axis
% % % limits can be differenet values.
% % %
% % % default is same_ylim=1;
% %
% % min_peak=100; % is the peak value in dB that is the
% % % minimum for being considered a peak.
% %
% % ignore_threshold=0; % 1 surpresses the threshold and other requirements
% % % for selecting peaks
% % % ignore_threshold=1; % generally will find more peaks.
% % %
% % % 0 is the default. Returns typical number of
% % % peaks.
% % %
% % % default is ignore_threshold=0;
% %
% % plot_str={'Protected', 'Unprotected'};
% % % Add a string to each subaxes indicating the
% % % meaning of the data. For hearing protector
% % % research one microphone is under the
% % % hearing protector so it is 'Protected'
% % % and the other microphone is exposed so it is
% % % 'Unprotected'.
% %
% % Align_peaks=1; % Specifies which channel to align all of the
% % % peaks. can be empty then all peaks are
% % % found independently.
% %
% % portrait_landscape=1; % 1 is for Potrait
% % % Otherwise Landscape
% %
% % peak_alignment_tol=0.25; % This input sets the fraction of the
% % % datapoints of num_pts_per_pk_intrl that
% % % a peak can be offset. When aligning
% % % peaks, the channels are synchronized,
% % % but temorally the peaks may be
% % % misaligned by a few milliseconds. The
% % % alignment tolerance allows the program to
% % % find the maximum peak within the
% % % alignment tolerance.
% % % default is 0.25.
% %
% % sod=0; % 1 surpress outlier detection
% % % 0 find the outliers and remove them from the
% % % statistical analysis
% % %
% % % default is sod=1;
% %
% % Polarized_or_ICP=0; % 1 Multiplies the time record p by -1 to correct for the
% % % voltage inversion of polarized microphones.
% % % 0 does not alter the time record, p.
% % % (ICP, IEPE, Deltatron microphones)
% % %
% % % default value is Polarized_or_ICP=0;
% %
% %
% % ********************************************************************
% %
% % Output Variables
% %
% % s % Is a cell array of structures.
% % %
% % % Each File and variable has its own cell and inside that cell
% % % is s structure containing the metrics and
% % % a description of the metrics data.
% % %
% % % The cells are arranged by [File_number Variable_Number]
% % % So the structures are accessed by
% % %
% % % s{file, var}.fieldname
% % %
% % % The structure inside each cell contains the following fields
% % %
% % % s{file, var}.filename is a char of the filename of the data
% % % file which was processed.
% % %
% % % s{file, var}.variable is a char of the vriable name of the
% % % variable within the filename of the data file
% % % which was processed.
% % %
% % % s{file, var}.metrics is a cell array of numeric array of
% % % metrics data for each identified impulsive peak
% % % for each channel.
% % %
% % % size(s{1,1}.metrics)=[num_channels 1]
% % % size(s{1,1}.metrics{num_channel, 1})=[num_peaks, num_metrics]
% % %
% % % s{file, var}.stats_of_metrics contains seven descriptive
% % % statistical quantities for each metric and channel
% % % size(s{1,1}.stats_of_metrics)=[num_metrics num_channels num_stats]
% % %
% % % The ststistical Quantities are
% % % Arithmetic Mean
% % % Robust Mean
% % % Standard Deviation
% % % 95 Confidence Interval
% % % Median
% % % Index Median Value
% % % Minimum
% % % Maximum
% % %
% % % s{file, var}.metrics_description isd a cell array of strings
% % % which describe the metric and its units
% % %
% % % s{1,1}.metrics_description{:, 1} descriptive strings
% % % s{1,1}.metrics_description{:, 2} units strings
% % %
% % % s{file, var}.stats_description is a cell array containing the
% % % description for each of the descriptive statistical
% % % quntities which are
% % % Arithmetic Mean
% % % Robust Mean
% % % Standard Deviation
% % % 95 Confidence Interval
% % % Median
% % % Index Median Value
% % % Minimum
% % % Maximum
% % %
% % % s{file, var}.diff_chan is a row vector containing the channel
% % % numbers of the channels where the row vector is a
% % % paired ordering of channel numbers.
% % % Each pair contains a Primary channel and a Minus
% % % channel.
% % %
% % % The mean of the metrics of the Minus channel are
% % % subtracted from the mean of the metrics of Primary
% % % channel.
% % % (For the peak values in (Pa) a ratio is computed.)
% % %
% % % s{file, var}.diff_metrics is a cell array containing numeric
% % % arrays of the differences in the mean metrics.
% % % size(s{1,1}.diff_metrics)=[num_peaks, num_metrics];
% % %
% % % The mean of the metrics of the Minus channel are
% % % subtracted from the mean of the metrics of Primary
% % % channel.
% % % (For the peak values in (Pa) a ratio is computed.)
% % %
% % % s{file, var}.diff_stats_of_metrics is a numeric array of
% % % descriptive statistics for the diff_metrics.
% % %
% % % size(s{1,1}.diff_stats_of_metrics)=[num_metrics num_pairs_diff_chan num_stats];
% % %
% % % s{file, var}.num_samples is a column vector of the number of
% % % in each channel.
% %
% %
% % 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;
% %
% %
% % ********************************************************************
%
%
%
% Example='1';
%
% % Create a data file;
% Fs_SP=100000; fc=1000; td=1; tau=0.01; delay=0.1; A1=2; A2=20;
% [SP, t]=analytic_impulse(Fs_SP, fc, td, tau, delay, A1, A2);
% save('default_data_file.mat', 'SP', 'Fs_SP');
%
% % Make appropriate settings for analyzing the default data file.
%
% filenamesin={'default_data_file.mat'};
% % cell array of strings of filenames to
% % process. These files can be matlab files
% % or wave files
%
% fileout_txt='fileout.txt'; % string filename to save data to a tab
% % delimited text file
%
% fileout_struct='fileout.mat'; % string filename to save data to a
% % matlab structure file
%
% save_struct=1; % 1 save all data to a matlab structure
% % The structure contains all of the impulsive
% % noise data from each of the impulses in each
% % channel in each file.
% % 0 do not save data to a matlab
% % structure
%
% peak_identification_time=1; % 1 seconds. Time interval of data to
% % identify each peak. Only one peak can
% % exist in each interval of this time
% % length.
%
% peak_metric_time=1; % 1 seconds. Time interval of data to
% % analyze each peak. Amount of data for
% % calculating metrics and Leqs.
%
% make_plot=1; % 1 to make plots for each channel analyzed.
% % otherwise will not make any plots
%
% save_plot=1; % 1 will save a plot.
% % otherwise will not not save plots
% % plots will not be saved if they are not made
% % the file name of hte plot is the
% % filename of the data file with the string
% % '_peaks' appended at the end.
%
% dB_scale=0; % 1 use a dB scale to plot sound time record
% % 0 use a linear scale to plot sound time record
%
% fig_format=[1,2,3,4,5,6]; % image format for saving the plot
% % 1 pdf default is 1 suggested
% % for simple documentation
% % 2 fig
% % 3 jpg (200 dpi resolution)
% % 4 eps2
% % 5 tiff (200 dpi resolution)
% % 6 tiff (no compression) suggested for
% % publications)
%
% Tool_Name='Hammer Drill'; % string input for the Name of the
% % test device.
%
% same_ylim=1; % 1 will set all of the ylimits of each of the
% % channels to the same value.
%
% min_peak=100; % (dB) is the peak value in dB that is the minimum
% % for being consiedered a peak.
%
% ignore_threshold=0; % 1 surpresses the threshold and other requirements
% % for selecting peaks
% % ignore_threshold=1; % generally will find more peaks.
% %
% % 0 is the default. Returns typical number of
% % peaks.
% %
% % default is ignore_threshold=0;
%
% plot_str={'Protected', 'Unprotected'};
% % Add a string to each subaxes indicating the
% % meaning of the data. For hearing protector
% % research one microphone is under the
% % hearing protector so it is 'Protected'
% % and the other microphone is exposed so it is
% % 'Unprotected'.
%
% Align_peaks=1; % Specifies which channel to align all of the
% % peaks. Can be empty then all peaks are
% % found independently.
%
% portrait_landscape=1; % 1 is for Portrait
% % Otherwise Landscape3
%
% peak_alignment_tol=0.25;
% % This input sets the fraction of the datapoints
% % of num_pts_per_pk_intrl that a peak can be offset.
% % When aligning peaks, the channels are
% % synchronized, but temorally the peaks may be
% % misaligned by a few milliseconds. The
% % alignment tolerance allows the program to find
% % the maximum peak within the alignment
% % tolerance.
% %
% % default is peak_alignment_tol=0.25;
% %
% % Further explanation of peak_alignment_tol
% % One-half of the datapoints of num_pts_per_pk_intrl are
% % used for finding the index of the maximum sound
% % pressure amplitude.
% %
% % The impulsive peak is found by selecting the highest
% % sound pressure amplitude starting at the center of
% % the bin and working toward the maximum radius which
% % is one-half of teh data points for the impulse.
%
% sod=0; % 1 surpress outlier detection
% % 0 find the outliers and remove them from the
% % statistical analysis
% % default is sod=1;
%
% Polarized_or_ICP=0; % 1 Multiplies the time record p by -1 to correct for the
% % voltage inversion of polarized microphones.
% % 0 does not alter the time record, p.
% % (ICP, IEPE, Deltatron microphones)
%
% [s]=Impulsive_Noise_Meter(filenamesin, fileout_txt, fileout_struct, save_struct, peak_identification_time, peak_metric_time, make_plot, save_plot, dB_scale, fig_format, Tool_Name, same_ylim, min_peak, ignore_threshold, plot_str, Align_peaks, portrait_landscape, peak_alignment_tol, sod, Polarized_or_ICP );
%
%
% % ********************************************************************
%
%
%
% Example='2';
%
% % Simulate 8 impulses in 4 microphones with different time records
%
% Fs_SP=100000; fc=1000; td=1; tau=0.01; delay=0.1; A1=8; A2=21; rt=0.0005;
%
% [SP1, t]=analytic_impulse(Fs_SP, fc, td, tau, delay, A1, A2, rt);
% Fs_SP=100000; fc=1000; td=1; tau=0.1; delay=0.1; A1=2; A2=22;
% [SP2, t]=analytic_impulse(Fs_SP, fc, td, tau, delay, A1, A2, rt);
% Fs_SP=100000; fc=1000; td=1; tau=0.4; delay=0.1; A1=2; A2=17;
% [SP3, t]=analytic_impulse(Fs_SP, fc, td, tau, delay, A1, A2, rt);
% Fs_SP=100000; fc=2000; td=1; tau=0.1; delay=0.1; A1=8; A2=15;
% [SP4, t]=analytic_impulse(Fs_SP, fc, td, tau, delay, A1, A2, rt);
% SP=[[0.9*SP4 SP1 0.3*SP3 SP2 0.5*SP4 0.2*SP1 SP3 5*SP2];...
% [SP1 0.7*SP4 10*SP2 SP3 0.4*SP2 0.5*SP3 SP1 1*SP4];...
% [SP3 0.2*SP1 0.5*SP4 2*SP2 0.4*SP2 0.7*SP3 0.3*SP1 10*SP4];...
% [SP1 0.7*SP2 0.3*SP3 SP4 0.4*SP1 10*SP2 SP3 1*SP4]];
%
% % Subtract the running mean value
% [SP]=sub_mean2(SP, Fs_SP, 5);
%
% % save the SP data variable to a matfile data file
%
% save('default_data_file.mat', 'SP', 'Fs_SP');
%
% % save the data to a wav file
%
% cf=1/(10*max(max(max(abs(SP)))));
% [pp, buf, buf, rdr]=sd_round(cf, 5);
% numstr=eval(['sprintf( ', ' ''%9.', num2str(rdr), 'f''', ', pp )']);
% fprintf(1, '%s\r\n', ['When Prompted use the calibration factor ', numstr]);
%
% wavwrite(cf*SP, Fs_SP, 16, 'default_data_file.wav');
%
% % Run the Main_Sound just respond to the prompts!
%
% Main_Sound;
%
%
%
% % ********************************************************************
%
%
% Example='3';
%
% Try to run the Impulsive_Noise_Meter from the Command Line
%
% Fs_SP=100000; fc=1000; td=1; tau=0.01; delay=0.1; A1=8; A2=21; rt=0.0005;
% [SP1, t]=analytic_impulse(Fs_SP, fc, td, tau, delay, A1, A2, rt);
% Fs_SP=100000; fc=1000; td=1; tau=0.1; delay=0.1; A1=2; A2=22;
% [SP2, t]=analytic_impulse(Fs_SP, fc, td, tau, delay, A1, A2, rt);
% Fs_SP=100000; fc=1000; td=1; tau=0.4; delay=0.1; A1=2; A2=17;
% [SP3, t]=analytic_impulse(Fs_SP, fc, td, tau, delay, A1, A2, rt);
% Fs_SP=100000; fc=2000; td=1; tau=0.1; delay=0.1; A1=8; A2=15;
% [SP4, t]=analytic_impulse(Fs_SP, fc, td, tau, delay, A1, A2, rt);
% SP=[[0.9*SP4 SP1 0.3*SP3 SP2 0.5*SP4 0.2*SP1 SP3 5*SP2];...
% [SP1 0.7*SP4 10*SP2 SP3 0.4*SP2 0.5*SP3 SP1 1*SP4];...
% [SP3 0.2*SP1 0.5*SP4 2*SP2 0.4*SP2 0.7*SP3 0.3*SP1 10*SP4];...
% [SP1 0.7*SP2 0.3*SP3 SP4 0.4*SP1 10*SP2 SP3 1*SP4]];
%
% simulate 8 impulses in 4 microphones with different time records
%
%
% % save the SP data variable to a matfile data file
%
% save('default_data_file.mat', 'SP', 'Fs_SP');
%
% % save the data to a wav file
%
% cf=1/(10*max(max(max(abs(SP)))));
% [pp, buf, buf, rdr]=sd_round(cf, 5);
% numstr=eval(['sprintf( ', ' ''%9.', num2str(rdr), 'f''', ', pp )']);
% fprintf(1, '%s\r\n', ['When Prompted use the calibration factor ', numstr]);
%
% wavwrite(cf*SP, Fs_SP, 16, 'default_data_file.wav');
%
% filenamesin={'default_data_file.mat', 'default_data_file.wav'};
% fileout_txt='fileout.txt';
% fileout_struct='fileout.mat';
% save_struct=1;
% peak_identification_time=0.5;
% peak_metric_time=0.1;
% make_plot=1;
% save_plot=1;
% dB_scale=1;
% fig_format=[1];
% Tool_Name='Hammer';
% same_ylim=1;
% min_peak=100;
% ignore_threshold=0;
% plot_str={'Protected', 'Unprotected'};
% Align_peaks=[];
% portrait_landscape=1;
% peak_alignment_tol=0.25;
% sod=0;
% Polarized_or_ICP=0;
%
% % [s]=Impulsive_Noise_Meter(filenamesin, fileout_txt, fileout_struct, save_struct, peak_identification_time, peak_metric_time, make_plot, save_plot, dB_scale, fig_format, Tool_Name, same_ylim, min_peak, ignore_threshold, plot_str, Align_peaks, portrait_landscape, peak_alignment_tol, sod, Polarized_or_ICP );
%
%
% % ********************************************************************
%
% Example='4';
%
% Try to analyze the Guns_example.wav using the command line with
% the Impulsive_Noise_Meter
%
% When prompted use a calibration factor of 0.000001
%
% Note that mic 2 does not have adequate dynamic resolution
%
% filenamesin={'Guns_example.wav'};
% fileout_txt='fileout.txt';
% fileout_struct='fileout.mat';
% save_struct=1;
% peak_identification_time=0.1;
% peak_metric_time=0.1;
% make_plot=1;
% save_plot=1;
% dB_scale=0;
% fig_format=[1];
% Tool_Name='Guns';
% same_ylim=1;
% min_peak=100;
% ignore_threshold=0;
% plot_str={'Unprotected', 'Protected'};
% Align_peaks=1;
% portrait_landscape=1;
% peak_alignment_tol=0.25;
% sod=0;
% Polarized_or_ICP=zeros(2, 1);
%
% [s, round_kind, round_digits]=Impulsive_Noise_Meter(filenamesin, fileout_txt, fileout_struct, save_struct, peak_identification_time, peak_metric_time, make_plot, save_plot, dB_scale, fig_format, Tool_Name, same_ylim, min_peak, ignore_threshold, plot_str, Align_peaks, portrait_landscape, peak_alignment_tol, sod, Polarized_or_ICP );
%
%
% % ********************************************************************
%
%
% Example='5';
%
% Run the Main_Sound and open the Guns_example.wav this time!
%
%
% Main_Sound;
%
%
% % ********************************************************************
% %
% % References:
% %
% %
% % ANSI S1.4-1983 American National Standard
% % Specificaitons for Sound Level Meters
% %
% % ANSI S1.6-1984 Preferred Frequencies, Frequency Levels, and Band
% % Numbers for Acoustical Measurements
% %
% % ANSI S1.43-1997 American National Standard
% % Specificaitons for Integrating-Averaging Sound Level
% % Meters
% %
% % ANSI S1.11-1986 American National Standard
% % Specification for Octave-band and Fractional-Octave-
% % band Analog and Digital Filters
% %
% % ANSI S3.44-1986 American National Standard
% % Determination of Occupational Noise Exposure and
% % Estimation of Noise Induced Hearing Impairment
% %
% %
% % Mil Standard 1474D, DEPARTMENT OF DEFENSE
% % DESIGN CRITERIA STANDARDm, NOISE LIMITS, 12 February 1997
% % www.silencertests.com/docs/mil-std-1474d.pdf
% %
% % Guido F. Smoorenburg, "Damage Risk Criteria for Impulsive
% % Noise," New Perspectives on Noise Induced Hearing Loss,
% % Raven Press, New York, pages(471-490) 1982
% %
% % ********************************************************************
% %
% %
% % Subprograms
% %
% % This program requires the Matlab Signal Processing Toolbox
% % This program contains code based on oct3dsgn by Christophe Couvreur 69
% %
% %
% %
% %
% % List of Dependent Subprograms for
% % Impulsive_Noise_Meter
% %
% % FEX ID# is the File ID on the Matlab Central File Exchange
% %
% %
% % Program Name Author FEX ID#
% % 1) A_duration Edward L. Zechmann
% % 2) abcd_durations Edward L. Zechmann
% % 3) ACdsgn Edward L. Zechmann
% % 4) ACweight_time_filter Edward L. Zechmann
% % 5) allstats Duane Hanselman NA
% % 6) analytic_impulse Edward L. Zechmann
% % 7) B_duration Edward L. Zechmann
% % 8) B_Duration_second_flucts Edward L. Zechmann
% % 9) B_mil_1474D_duration Edward L. Zechmann
% % 10) bessel_antialias Edward L. Zechmann
% % 11) bessel_digital Edward L. Zechmann
% % 12) bessel_down_sample Edward L. Zechmann
% % 13) C_duration Edward L. Zechmann
% % 14) calc_impuls_threshold_and_index Edward L. Zechmann
% % 15) convert_double Edward L. Zechmann
% % 16) D_duration Edward L. Zechmann
% % 17) data_loader2 Edward L. Zechmann
% % 18) data_outliers3 Edward L. Zechmann
% % 19) file_extension Edward L. Zechmann
% % 20) filter_settling_data3 Edward L. Zechmann
% % 21) find_previous_crossing Edward L. Zechmann
% % 22) findextrema Schuberth Schuberth 3586
% % 23) fix_YTick Edward L. Zechmann
% % 24) genHyper Ben Barrowes 6218
% % 25) geomean2 Edward L. Zechmann
% % 26) geospace Edward L. Zechmann
% % 27) get_p_q2 Edward L. Zechmann
% % 28) kurtosis2 William Murphy
% % 29) Leq_all_calc Edward L. Zechmann
% % 30) LMS_trim Edward L. Zechmann
% % 31) LMSloc Alexandros Leontitsis 801
% % 32) LMTSregor Edward L. Zechmann
% % 33) localpeaks Edward L. Zechmann
% % 34) m_round Edward L. Zechmann
% % 35) match_height_and_slopes2 Edward L. Zechmann
% % 36) moving Aslak Grinsted 8251
% % 37) nth_freq_band Edward L. Zechmann
% % 38) Nth_oct_time_filter2 Edward L. Zechmann
% % 39) Nth_octdsgn Edward L. Zechmann
% % 40) parseArgs Malcolm Wood 10670
% % 41) peak_index Edward L. Zechmann
% % 42) peak_threshhold_function Edward L. Zechmann
% % 43) percentiles Edward L. Zechmann
% % 44) plot_snd_vibs Edward L. Zechmann
% % 45) plotpeaks Edward L. Zechmann
% % 46) pow10_round Edward L. Zechmann
% % 47) print_data_loader_configuration_table Edward L. Zechmann
% % 48) print_outliers_indices Edward L. Zechmann
% % 49) psuedo_box Edward L. Zechmann
% % 50) rand_int Edward L. Zechmann
% % 51) remove_filter_settling_data Edward L. Zechmann
% % 52) resample_interp3 Edward L. Zechmann
% % 53) resample_plot Edward L. Zechmann
% % 54) rise_time Edward L. Zechmann
% % 55) rms_val Edward L. Zechmann
% % 56) save_a_plot2_audiological Edward L. Zechmann
% % 57) sd_round Edward L. Zechmann
% % 58) snd_peak_metrics Edward L. Zechmann
% % 59) sub_mean Edward L. Zechmann
% % 60) sub_mean2 Edward L. Zechmann
% % 61) subaxis Aslak Grinsted 3696
% % 62) t_alpha Edward L. Zechmann
% % 63) t_confidence_interval Edward L. Zechmann
% % 64) t_icpbf Edward L. Zechmann
% % 65) tableGUI Joaquim Luis 10045
% % 66) threshold_bin_peaks Edward L. Zechmann
% %
% %
% % ********************************************************************
% %
% % Impulsive_Noise_Meter is Written by Edward L. Zechmann
% %
% % date 10 August 2007
% %
% % modified 18 December 2007 Multitude of updates. brief list of
% % major changes
% % updated the a,b,c,d duration
% % calculations
% % added the data_outliers
% % added data_loader
% % added and updated sub_mean
% % added geomean2
% % updated plot_snd_vibs
% % updated save_a_plot2_audiological
% %
% % modified 21 December 2007 Updated comments
% %
% % modified 27 December 2007 Updated peak finding process in
% % localpeaks.m
% % fixed pts_per_bin to 0.01 seconds worth
% % of data points
% %
% % modified 27 December 2007 Updated peak finding process
% % in localpeaks.m
% % fixed pts_per_bin to 0.01 seconds
% % worth of data points
% %
% % modified 8 January 2008 Updated data_loader2. Loading matlab
% % data files now prmpts the user to
% % configure the variables.
% % There is an option to use the same
% % configuration for each data file.
% %
% % modified 21 February 2008 Updated comments.
% %
% % modified 26 February 2008 Modified the array_units, updated
% % local_peaks.m, updated comments to
% % several sub programs.
% %
% % modified 17 March 2008 Added peak_alignment_tol option for
% % specifying the tolerance for aligning
% % the peaks as a fraction of the
% % peak_identification_time.
% %
% % modified 18 August 2008 Updated Comments.
% % Added third oct linear peaks
% %
% % modified 19 August 2008 Added input variable percent1
% % to specify threshold for
% % identifying peaks
% %
% % modified 10 September 2008 Added optional method for finding
% % impulsive Exhaust Noise
% %
% % modified 22 September 2008 Updated Comments.
% %
% % modified 10 December 2008 Upgraded the third octave band
% % filtering programs and the A and C-
% % weighting filter programs to include
% % filter settling and resampling.
% %
% % modified 11 December 2008 Upgraded the A and C-
% % weighting filter programs,
% % to include iterative filtering.
% % The filters are now very stable.
% %
% % Removed filter coefficients from input
% % and output;
% % Peaks pressures and Levels are output.
% %
% % modified 16 December 2008 For the A and C weighting filters,
% % Use convolution to make filter
% % coefficients (b and a) into
% % arrays from cell arrays .
% %
% % modified 5 January 2009 Added sub_mean to the Nth octave
% % band filters and A and C weighting
% % filters.
% %
% % modified 21 March 2009 Added the B-Mil-Std-1474-D-Duration
% % Added the peak analysis time for
% % calculating metrics.
% % Added a flag to data_outliers3 to
% % convert dB data to Pa before
% % calculating mean and median values.
% % Updated comments
% %
% % modified 22 March 2009 Added db_scale to select dB or linear
% % scale for plotting the sound time
% % record.
% % Updated Comments.
% %
% % modified 6 October 2009 Updated comments
% %
% % modified 5 August 2010 Added resampling using Bessel
% % antialiasing filter
% % Updated Comments
% %
% % modified 10 August 2010 Added rise time
% % Updated Comments
% %
% % modified 11 October 2010 Changed peak finder to peak_ix
% % Updated comments.
% %
% % modified 11 February 2011 Updated to new method of finding peaks.
% % Updated Comments.
% %
% %
% %
% % ********************************************************************
% %
% % Please Feel Free To Modify This Code
% %
% % See also: Continuous_Sound_and_Vibrations_Analysis, snd_peak_metrics, plot_peaks, local_peaks,
% %
if (nargin < 1 || isempty(filenamesin)) || ischar(filenamesin) || (~iscell(filenamesin) && numel(filenamesin) > 1)
[filenamesin, pathname, filterindex] = uigetfile( { '*.mat'; '*.wav'}, 'Select the files To Process', 'MultiSelect', 'on');
if isempty(filenamesin) || ischar(filenamesin) || (~iscell(filenamesin) && numel(filenamesin) > 1)
% expecting a cell array of strings
% if only one file is selected then it will be a character array or a
% single string
% convert into a cell array if necessary
if ~isempty(filenamesin) && ischar(filenamesin) && isequal(exist(filenamesin, 'file'), 2)
filenamesin={filenamesin};
else
if isempty(filenamesin)
[filenamesin, pathname] = uigetfile( { '*.mat';'*.wav'}, 'Select the files To Process', 'MultiSelect', 'on');
end
if ischar(filenamesin) && isequal(exist(filenamesin, 'file'), 2)
filenamesin={filenamesin};
end
end
pathname=cd;
cd(pathname);
else
cd( pathname);
end
end
if ~iscell(filenamesin) || isempty(filenamesin) || length(filenamesin) < 1
error('Must select at least 1 file to analyze');
end
if ~iscell(filenamesin) || isempty(filenamesin) || length(filenamesin) < 1
error('Must select at least 1 file to analyze');
end
filenamesin=sort(filenamesin);
if (nargin < 2 || isempty(fileout_txt)) || ~ischar(fileout_txt)
fileout_txt='data1.txt';
end
if (nargin < 3 || isempty(fileout_struct)) || ~ischar(fileout_struct)
fileout_struct='data1_struct';
end
if (nargin < 4 || isempty(save_struct)) || ~isnumeric(save_struct)
save_struct=1;
end
if (nargin < 5 || isempty(peak_identification_time)) || ~isnumeric(peak_identification_time)
peak_identification_time=1;
end
if (nargin < 6 || isempty(peak_metric_time)) || ~isnumeric(peak_metric_time)
peak_metric_time=1;
end
if (nargin < 7 || isempty(make_plot)) || ~isnumeric(make_plot)
make_plot=1;
end
if (nargin < 8 || isempty(save_plot)) || ~isnumeric(save_plot)
save_plot=1;
end
if nargin < 9 || isempty(dB_scale) || ~isnumeric(dB_scale)
dB_scale=0;
end
if (nargin < 10 || isempty(fig_format)) || ~isnumeric(fig_format)
fig_format=1;
end
if (nargin < 11 || ~ischar(Tool_Name))
Tool_Name='Impulsive Noise';
end
if (nargin < 12 || isempty(same_ylim)) || ~isnumeric(same_ylim)
same_ylim=1;
end
if (nargin < 13 || isempty(min_peak)) || ~isnumeric(min_peak)
min_peak=100;
end
if (nargin < 14 || isempty(ignore_threshold)) || ~isnumeric(ignore_threshold)
ignore_threshold=0;
end
if (nargin < 15 || ~iscell(plot_str))
plot_str={'Unprotected', 'Protected'};
end
if (nargin < 16 || isempty(Align_peaks)) || ~isnumeric(Align_peaks) || logical(Align_peaks < 1)
Align_peaks=[];
end
if (nargin < 17 || isempty(portrait_landscape)) || ~isnumeric(portrait_landscape)
portrait_landscape=1;
end
if (nargin < 18 || isempty(peak_alignment_tol)) || ~isnumeric(peak_alignment_tol)
peak_alignment_tol=0.25;
end
if nargin < 19 || isempty(sod) || ~isnumeric(sod)
sod=1;
end
if nargin < 20 || isempty(Polarized_or_ICP) || ~isnumeric(Polarized_or_ICP)
Polarized_or_ICP=0;
end
num_files=length(filenamesin);
% Make sure the .txt extension is added
[filename_base, ext]=file_extension(fileout_txt);
fileout_txt=[filename_base, '.txt'];
fid=fopen(fileout_txt, 'w');
filename=cell(num_files,1);
filenum_array=ones(num_files,1);
metrics={};
durations_array=cell(num_files,1);
other_stats2=cell(num_files,1);
% Variables may be called using eval
a=[];
b=[];
ba=[];
stat_str={'Arithmetic Mean', 'Robust Mean', 'Standard Deviation','95% Confidence Interval','Median Index','Median Value','Minimum','Maximum'};
num_stats=length(stat_str);
row_names={'Channel Number'};
row_unit='';
col_name='Peak Number';
%array_names={'Peak Level Linear', 'Peak Index', 'Peak Time', 'Peak Pres. Linear', 'Peak Pres. A-Weight', 'Peak Pres. C-Weight', 'Peak Level Linear ', 'Peak Level A-Weight )', 'Peak Level C-Weight ', 'LeqA', 'LeqA8', 'LeqC', 'LeqC8', 'Leq', 'Leq8', 'Time Span', 'Rise Time', 'A-Duration', 'B-Duration', 'C-Duration', 'D-Duration', 'Kurtosis'};
%array_units={'(dB)', '(Indices)', '(s)', '(Pa)', '(Pa)', '(Pa)', '(dB)', '(dBA)', '(dBC)', '(dBA)','(dBA)', '(dBC)','(dBC)', '(dB)','(dB)', '(s)', '(s)', '(s)', '(s)', '(s)', '(s)', '(No units)'};
metrics={};
metric_str={};
metric_units={};
AHAAH_str={};
other_stats={};
md={};
% initialize the input configuration cell arrays for the wav files
% and the matlab data files
default_wav_settings_in={};
default_mat_config_in={};
% initialize the data set counter
e1=0;
s={};
for e6=1:num_files;
SP=[];
% load the data file
[SP_var, vibs_var, Fs_SP_var, Fs_vibs_var, default_wav_settings_out, default_mat_config_out]=data_loader2(filenamesin{e6}, default_wav_settings_in, default_mat_config_in);
% set the default wav file setttings
% for configuring the channels
default_wav_settings_in=default_wav_settings_out;
default_mat_config_in=default_mat_config_out;
if iscell(SP_var)
num_vars=length(SP_var);
else
num_vars=1;
end
for e7=1:num_vars;
if iscell(SP_var)
if length(SP_var) >= e7
SP=SP_var{e7};
end
if length(Fs_SP_var) >= e7
Fs_SP=Fs_SP_var{e7};
end
% currently there is not any code for impulsive vibrations
% analysis
%if length(vibs_var) >= e7
% vibs=vibs_var{e7};
%end
%
%if length(Fs_vibs_var) >= e7
% Fs_vibs=Fs_vibs_var{e7};
%end
else
SP=SP_var;
Fs_SP=Fs_SP_var;
%vibs=vibs_var;
%Fs_vibs=Fs_vibs_var;
end
% make sure that input data is double precision.
[SP]=convert_double(SP);
[Fs_SP]=convert_double(Fs_SP);
[m1 n1]=size(SP);
if m1 > n1
SP=SP';
[m1 n1]=size(SP);
end
% reinitialize the durations cell array
metrics={};
% increment the counter which counts all of the variables which
% have been processed
e1=e1+1;
% sub_mean2 program interpolates to create a running average
% pressure then subtracts the running average pressure
if max(size(SP)) > 0
[SP]=sub_mean2(SP, Fs_SP, 15);
end
% Currently this program does not contian any programs
% specifically for impulsive vibrations.
%
%if max(size(vibs)) > 0
% [vibs]=vibs-mean(vibs, 2)*ones(1, size(vibs, 2));
%end
metrics={};
if exist('SP','var') == 1 && length(SP) > 1
filename1=filenamesin{e6};
fprintf(1, '%s\r\n', ['Processing file ', num2str(e6), ' ', filename1, ' Variable ', num2str(e7) ]);
% minimum peak preessure to be an impulsive noise
num_pts_per_pk_intrl=floor(peak_identification_time*Fs_SP);
if num_pts_per_pk_intrl < 1
num_pts_per_pk_intrl=1;
end
if num_pts_per_pk_intrl > max(size(SP))
num_pts_per_pk_intrl=max(size(SP));
end
pts_per_pk_metrics=floor(peak_metric_time*Fs_SP);
% ************************************************************
%
% plotpeaks will run peak-ix which finds teh peaks
% plot peaks will then plot the peaks on a figure window
[h, h2, indices2]=plotpeaks(SP, Fs_SP, num_pts_per_pk_intrl, make_plot, Tool_Name, [filename1 ' var ', num2str(e7)], same_ylim, min_peak, ignore_threshold, plot_str, Align_peaks, peak_alignment_tol, dB_scale );
% ************************************************************
%
% snd_peak_metrics will calculate the impulsive metrics
% This program calculates all of the impulsive noise metrics
N=3;
min_f=20;
max_f=20000;
ear_model=ones(m1, 1);
[metrics, metric_str, metric_units, AHAAH_str, round_kind, round_digits, dB_or_linear]=snd_peak_metrics(SP, Fs_SP, indices2, pts_per_pk_metrics, N, min_f, max_f, ear_model, Polarized_or_ICP);
round_kind2=[1 round_kind];
round_digits2=[3 round_digits];
dB_or_linear=[1 dB_or_linear];
% Get the number of metrics
num_metrics=size(metrics{1, 1}, 2);
% Get the number of channels
% metrics is a cell array of size [num_channels, 1]
% each cell within metrics contains a 2d array of metrics
% of size {num_impulses, num_metrics}
msd=size(metrics, 1);
figure(1);
for e2=1:length(h2);
axes(h2(e2));
end
% Save the plot of the time rcords identifying the impulsive peaks
% change the filename for each file and variable.
if save_plot == 1
for e2=1:length(fig_format);
save_a_plot2_audiological(fig_format(e2), [filename1(1:(length(filename1)-4)), '_peaks_var_', num2str(e7) ], portrait_landscape);
end
end
fprintf(fid, 'filename\t%s\r\n', filename1);
fprintf(fid, 'Variable\t%s\r\n', num2str(e7) );
fprintf(fid, '\r\n');
abs_metrics=[3 4 5];
num_peaks=length(metrics{1}(:, 1));
ba2=cell(msd, num_metrics);
for e4=1:num_metrics;
for e2=1:msd;
num_peaks=length(metrics{e2}(:, 1));
buf=ones(num_peaks, 1);
for e3=1:num_peaks;
buf(e3, 1)=metrics{e2,1}(e3, e4);
end
ba2{e2, e4}=buf;
end
end
% use the data outliers program to statistically evaluate the data
% Call data_outliers using the cell array format which allows
% for a different number of peaks for each channel
% print data to a file using the fid
abs_rta=1;
abs_other=[ 0 0 1 1 1 zeros(1, num_metrics-5)];
metric_str2=cell(1, num_metrics+1);
metric_units2=cell(1, num_metrics+1);
metric_str2{1}=metric_str{6};
metric_units2{1}=metric_units{6};
for e4=1:num_metrics;
metric_str2{e4+1}=metric_str{e4};
metric_units2{e4+1}=metric_units{e4};
end
% Outliers are identified for all
% metric arrays independently.
dep_var=0;
function_str='data_outliers3(dB_or_linear, ba2(1:msd,6), dep_var, round_kind2, round_digits2, abs_rta, abs_other, sod, fid, 1, row_names, row_unit, col_name, metric_str2, metric_units2';
for e4=1:num_metrics;
function_str=[function_str, ', ba2(1:msd, ', num2str(e4), ')'];
end
function_str=[function_str,' )'];
[ptsa, nptsa, rt_stats, other_stats, rt_outlier_stats, other_outlier_stats]=eval(function_str);
fprintf(fid, '\r\n');
% These four lines of voided code are present to identify
% data_outliers as a dependent function.
void_call=0;
if void_call
data_outliers3(0);
end
% Print the same information from the data_outliers program,
% but print the data horizontally.
for e2=1:msd;
sizedur=size(metrics{e2});
fprintf(fid, ['Microphone Channel ' num2str(e2), ' \r\n']);
fprintf(fid, '\tPeak Number\t');
% Print the names of the metrics
for e3=1:length(metric_str);
fprintf(fid, '%s\t', metric_str{e3});
end
fprintf(fid,'\r\n\t\t');
% Print the units of the metrics
for e3=1:length(metric_str);
fprintf(fid, '%s\t', metric_units{e3});
end
fprintf(fid,'\r\n');
format_str='\t%i\t%i\t%f\t%3.2f\t%3.2f\t%3.2f\t%3.2f\t%3.2f\t%3.2f\t%3.2f\t%3.2f\t%3.2f\t%3.2f\t%3.2f\t%3.2f\t%f\t%f\t%f\t%f\t%f\t%3.2f';
for e3=1:(num_metrics-20);
format_str=[format_str, '\t%f'];
end
format_str=[format_str, '\t\r\n'];
fprintf(fid,'Data');
fprintf(fid, format_str, [(1:sizedur(1))' metrics{e2}]');
[m1, n11]=size(metrics{e2});
fprintf(fid,'Descriptive Statistics');
for e5=1:num_stats;
buf2=zeros(1, n11);
for e4=1:n11;
buf2(1, e4)=other_stats(e4, e2, e5);
end
fprintf(fid, '\t%s', stat_str{e5});
fprintf(fid, format_str(5:end), buf2');
end
fprintf(fid, '\r\n');
% List the indices of the data outlier
if ~isequal(sod, 1) && logical(numel(other_outlier_stats) >= max([n11*num_stats*msd, 1]))
fprintf(fid, '%s\t%s\t', 'Data Outliers', 'Microphone Indices');
[fid]=print_outliers_indices(nptsa(e2, 2:end), fid);
fprintf(fid, '\r\n');
fprintf(fid, '\r\n');
for e5=1:num_stats;
buf2=zeros(1, n11);
for e4=1:n11;
buf2(1, e4)=other_outlier_stats(e4, e2, e5);
end
fprintf(fid, '\t%s', stat_str{e5});
fprintf(fid, format_str(5:end), buf2');
end
fprintf(fid, '\r\n');
end
end
fprintf(fid, '\r\n');
fprintf(fid, '\r\n');
end
% md stands for "metrics description"
md=cell(2, length(metric_str));
for e2=1:length(metric_str);
md{1, e2}=metric_str{e2};
md{2, e2}=metric_units{e2};
end
% A cell array of data structures is formed to collect the data for each
% file and each variable for each peak.
s{e6, e7}.filename=filenamesin{e6};
s{e6, e7}.variable=e7;
s{e6, e7}.metrics=metrics;
s{e6, e7}.stats_of_metrics=other_stats;
s{e6, e7}.stats_of_outliers_metrics=other_outlier_stats;
s{e6, e7}.metrics_description=md;
s{e6, e7}.stats_description=stat_str;
end
end
if save_struct == 1
save(fileout_struct, 's');
end
fclose(fid);
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