| [xw, xp, xb, SPLw1, SPLp1, SPLb1, SPLw2, SPLp2, SPLb2, f ]=Test_Nth_octave_Band2(Fs, td, N, min_f, max_f, num_x_filter, sensor, settling_time, filter_program, resample_filter)
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function [xw, xp, xb, SPLw1, SPLp1, SPLb1, SPLw2, SPLp2, SPLb2, f ]=Test_Nth_octave_Band2(Fs, td, N, min_f, max_f, num_x_filter, sensor, settling_time, filter_program, resample_filter)
% % Test_third_oct_filters: Tests Nth octave filters with white, pink, and brown noise.
% %
% % Syntax:
% %
% % [xw, xp, xb, SPLw1, SPLp1, SPLb1, SPLw2, SPLp2, SPLb2, f ]=Test_Nth_octave_Band2(Fs, td, N, min_f, max_f, num_x_filter, sensor, settling_time, filter_program, resample_filter);
% %
% % **********************************************************************
% %
% % Description
% %
% % Program tests the spectra of the Nth octave band filters for
% % white, pink, and brown noise.
% %
% % A figure showing the three Nth octave band spectra is output.
% % Additionally the white, pink, and brown noise spectra are output
% % along with the frequency array.
% %
% % The input and output variables are described in more detail in the
% % sections below respectively.
% %
% % Nth_octdsgn computes the filter coefficients using a 3rd order
% % butterworth filter for an Nth octave band filter according to
% % ANSI S1.11.
% %
% % To optimize filter stability, this program uses iterative downsampling
% % to make the sampling rate reasonable before applying the third octave
% % Butterworth filters.
% %
% % There are two options for the downsampling filters to optimize
% % performance for continuous signals or for impulsive signals.
% % For continuous noise the time domain does not have significant
% % impulses; however, for impulsive time records there are often very
% % large impulses with distinctive peaks.
% %
% % There are two antialiasing filters and interpolation schemes available.
% % The first program is the built-in Matlab "resample" progam which
% % uses a Kaiser window fir filter for antialising and uses an unknown
% % interpolation method. The second program available for downsampling
% % is bessel_down_sample which uses a Bessel filter for antialiasing
% % and uses interp with the cubic spline option for interpolation.
% %
% % The resample function has good antialising up to the Nyquist frequency;
% % however, it has significant ringing effect when there are impulses.
% % The bessel_down_sample function has good antialising; however, there is
% % excessive attenuation near the Nyquist frequency.
% % The bessel_down_sample function experiences no ringing due to impulses
% % so it is very useful for peak estimation.
% %
% %
% % The input and output variables are described in more detail in the
% % respective sections below.
% %
% %
% % **********************************************************************
% %
% % Input Variables
% %
% % Fs=50000; % (Hz) sampling rate.
% % % default is Fs=50000;
% %
% % td=10; % (seconds) time duration of test signal.
% % % There are three test signal white, pink, and
% % % brown noise.
% % % default is td=10;
% %
% % N=3; % is the number of frequency bands per octave.
% % % Can be any number > 0.
% % % Default is N=3 for third octave bands.
% %
% % min_f=20; % is the minimum frequency band to calculate (Hz).
% % % Must be graeater than 0.
% % % default is min_f=20;
% %
% % max_f=20000; % max_f is the maximum frequency band to calculate
% % % (Hz). Must be graeater than 0.
% % % default is max_f=20000;
% %
% %
% % num_x_filter=2; % This is the number of times the time record
% % % should be filtered.
% % % default is num_x_filter=2;
% %
% % sensor=1; % acoustic microphone
% % % output is in dB
% % % default is sensor=1;
% %
% % settling_time=1; % (seconds) Time requiered for the filter to settle
% % % usually 0.1 seconds or less.
% % % This quantity is usually frequency dependent.
% % % settling_time=1;
% %
% % filter_program=1; % 1 is for using the filter progam otherwise the
% % % filtfilt program is used.
% % % filter.m runs faster and may settle
% % % more quickly.
% % % filtfilt.m is used to remove phase shift.
% % % default is filter_program=1, use filter progam.
% %
% % resample_filter=1; % type of filter to use when resampling
% % % 1 resample function Kaiser window fir filter
% % % 2 Bessel filter
% % % otherwise resample function Kaiser window fir
% % % filter
% % % default is resample_filter=1; (Kaiser window)
% %
% %
% % **********************************************************************
% %
% % Output Variables
% %
% % xw is the white noise output time record.
% %
% % xp is the pink noise output time record.
% %
% % xb is the brown noise output time record.
% %
% % SPLw1 is the white noise output spectra from Nth_oct_time_filter.
% %
% % SPLp1 is the pink noise output spectra from Nth_oct_time_filter.
% %
% % SPLb1 is the brown noise output spectra from Nth_oct_time_filter.
% %
% % SPLw2 is the white noise output spectra from Nth_oct_time_filter2.
% %
% % SPLp2 is the pink noise output spectra from Nth_oct_time_filter.
% %
% % SPLb2 is the brown noise output spectra from Nth_oct_time_filter.
% %
% % f is the Nth octave band output frequency spectra.
% %
% %
% % **********************************************************************
%
%
% Example='1';
%
% % Try running the program with no input variables and no output
% % variables.
% %
% % The brown noise should drop at a rate of 3 dB per octave.
% % The white noise should increase at 3 dB per octave.
% % The pink noise should be constant.
%
% Test_Nth_octave_Band2;
%
% % **********************************************************************
% %
% % References Standards
% %
% % ANSI S1.11-1986 Specifications for Octave-band and fractional-octave band
% % analog and digital filters.
% %
% % **********************************************************************
% %
% %
% %
% % List of Dependent Subprograms for
% % Test_Nth_octave_Band2
% %
% % FEX ID# is the File ID on the Matlab Central File Exchange
% %
% %
% % Program Name Author FEX ID#
% % 1) bessel_antialias Edward L. Zechmann
% % 2) bessel_digital Edward L. Zechmann
% % 3) bessel_down_sample Edward L. Zechmann
% % 4) convert_double Edward L. Zechmann
% % 5) filter_settling_data3 Edward L. Zechmann
% % 6) geospace Edward L. Zechmann
% % 7) LMSloc Alexandros Leontitsis 801
% % 8) match_height_and_slopes2 Edward L. Zechmann
% % 9) moving Aslak Grinsted 8251
% % 10) nth_freq_band Edward L. Zechmann
% % 11) Nth_oct_time_filter Edward L. Zechmann
% % 12) Nth_oct_time_filter2 Edward L. Zechmann
% % 13) Nth_octdsgn Edward L. Zechmann
% % 14) remove_filter_settling_data Edward L. Zechmann
% % 15) resample_interp3 Edward L. Zechmann
% % 16) rms_val Edward L. Zechmann
% % 17) sd_round Edward L. Zechmann
% % 18) spatialPattern Jon Yearsley 5091
% % 19) sub_mean Edward L. Zechmann
% %
% % **********************************************************************
% %
% % Program was written by Edward L. Zechmann
% %
% % date 22 December 2008 Created program
% %
% % modified 7 January 2008 Update Comments
% %
% % modified 4 August 2010 Updated Comments
% %
% %
% % **********************************************************************
% %
% %
% % Please feel free to modify this code.
% %
% % See Also: Nth_oct_time_filter, Nth_oct_time_filter2, octave, resample, filter, filtfilt
% %
if (nargin < 1 || isempty(Fs)) || ~isnumeric(Fs)
Fs=3;
end
if (nargin < 2 || isempty(td)) || ~isnumeric(td)
td=10;
end
if (nargin < 3 || isempty(N)) || ~isnumeric(N)
N=3;
end
if (nargin < 4 || isempty(min_f)) || (logical(min_f < 0) || ~isnumeric(min_f))
min_f=20;
end
if (nargin < 5 || isempty(max_f)) || (logical(max_f < 0) || ~isnumeric(max_f))
max_f=20000;
end
if (nargin < 6 || isempty(num_x_filter)) || ~isnumeric(num_x_filter)
num_x_filter=2;
end
if (nargin < 7 || isempty(sensor)) || ~isnumeric(sensor)
sensor=1;
end
if (nargin < 8 || isempty(settling_time)) || ~isnumeric(settling_time)
settling_time=0.1;
end
if (nargin < 9 || (isempty(filter_program)) || ~isnumeric(filter_program))
filter_program=1;
end
if (nargin < 10 || isempty(resample_filter)) || ~isnumeric(resample_filter)
resample_filter=1;
end
% Calculate the number of points in the time records
num_pts=ceil(Fs*td);
if num_pts < 100
num_pts=100;
end
% Compare the spectra of white noise, pink noise, and brown noise.
% % **********************************************************************
%
% Create the time records for the three type of noise
%
xw = spatialPattern([1,num_pts],0); % white noise has a linearly
% increasing spectrum
xp = spatialPattern([1,num_pts],-1); % pink noise has a constant
% spectrum
xb = spatialPattern([1,num_pts],-2); % brown noise has a linearly
% decreasing spectra
% % **********************************************************************
%
% % Compute the Nth octave band spectra for the Nth_oct_time_filter
%
[f, SPLw1]=Nth_oct_time_filter(xw, Fs, num_x_filter, N, min_f, max_f, sensor, settling_time, filter_program, resample_filter);
[f, SPLp1]=Nth_oct_time_filter(xp, Fs, num_x_filter, N, min_f, max_f, sensor, settling_time, filter_program, resample_filter);
[f, SPLb1]=Nth_oct_time_filter(xb, Fs, num_x_filter, N, min_f, max_f, sensor, settling_time, filter_program, resample_filter);
% % **********************************************************************
%
% % Plot the results for the Nth_oct_time_filter
%
figure(1);
semilogx(f, SPLw1, 'color', [1 1 1], 'linewidth', 2, 'marker', 's', 'MarkerSize', 8);
hold on;
semilogx(f, SPLp1, 'color', [1 0.6 0.784], 'linewidth', 2, 'linestyle', '--', 'marker', 'o', 'MarkerSize', 8);
semilogx(f, SPLb1, 'color', [0.682 0.467 0], 'linewidth', 2, 'linestyle', ':', 'marker', 'x', 'MarkerSize', 12);
set(gca, 'color', 0.7*[1 1 1]);
legend({'White Noise', 'Pink Noise', 'Brown Noise'}, 'location', 'SouthEast');
xlabel('Frequency Hz', 'Fontsize', 28);
ylabel('Sound Pressure Level (dB ref. 20 \mu Pa)', 'Fontsize', 28);
title({'Nth oct time filter', 'Classical Third Octave Band Spectra'}, 'Fontsize', 40);
set(gca, 'Fontsize', 20);
fc=f;
% % **********************************************************************
%
% % Compute the Nth octave band spectra for the Nth_oct_time_filter2
%
[f, SPLw2]=Nth_oct_time_filter2(xw, Fs, num_x_filter, N, fc, sensor, settling_time, filter_program, resample_filter);
[f, SPLp2]=Nth_oct_time_filter2(xp, Fs, num_x_filter, N, fc, sensor, settling_time, filter_program, resample_filter);
[f, SPLb2]=Nth_oct_time_filter2(xb, Fs, num_x_filter, N, fc, sensor, settling_time, filter_program, resample_filter);
% % **********************************************************************
%
% % Plot the results for the Nth_oct_time_filter2
%
figure(2);
semilogx(f, SPLw2, 'color', [1 1 1], 'linewidth', 2, 'marker', 's', 'MarkerSize', 8);
hold on;
semilogx(f, SPLp2, 'color', [1 0.6 0.784], 'linewidth', 2, 'linestyle', '--', 'marker', 'o', 'MarkerSize', 8);
semilogx(f, SPLb2, 'color', [0.682 0.467 0], 'linewidth', 2, 'linestyle', ':', 'marker', 'x', 'MarkerSize', 12);
set(gca, 'color', 0.7*[1 1 1]);
legend({'White Noise', 'Pink Noise', 'Brown Noise'}, 'location', 'SouthEast');
xlabel('Frequency Hz', 'Fontsize', 28);
ylabel('Sound Pressure Level (dB ref. 20 \mu Pa)', 'Fontsize', 28);
title({'Nth oct time filter2', 'Classical Third Octave Band Spectra'}, 'Fontsize', 40);
set(gca, 'Fontsize', 20);
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