| [fc_out, SP_levels, SP_peak_levels, SP_bands]=Nth_oct_time_filter(SP, Fs, num_x_filter, N, min_f, max_f, sensor, settling_time, filter_program, resample_filter)
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function [fc_out, SP_levels, SP_peak_levels, SP_bands]=Nth_oct_time_filter(SP, Fs, num_x_filter, N, min_f, max_f, sensor, settling_time, filter_program, resample_filter)
% % Nth_oct_time_filter: Calculates the Nth octave center frequencies, sound levels, peak levels, and time records
% %
% % Syntax:
% %
% % [fc_out, SP_levels, SP_peak_levels, SP_bands]=Nth_oct_time_filter(SP, Fs, num_x_filter, N, min_f, max_f, sensor, settling_time, filter_program, resample_filter);
% %
% % **********************************************************************
% %
% % Description
% %
% % This program applies Nth octave band filters to the input time record.
% % The program outputs the center frequency bands, the time average rms
% % values, the peak values, and band filtered time records for each
% % Nth octave band respectively.
% %
% %
% % This program applies Nth octave band filters to the input time record.
% % The program outputs the center frequency bands, the time average rms
% % values, the peak values, and band filtered time records for each
% % Nth octave band 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.
% %
% %
% % To avoid phase shift, the filtfilt Matlab program can
% % be used to implement the one-Nth octave filters.
% %
% % The input and output variables are described in more detail in the
% % sections below respectively.
% %
% % **********************************************************************
% %
% % Input Variables
% %
% % SP=randn(10, 50000);
% % % (Pa) is the time record of the sound pressure
% % % default is SP=rand(1, 50000);
% %
% % Fs=50000; % (Hz) is the sampling rate of the time record.
% % % default is Fs=50000; Hz.
% %
% % num_x_filter=2; % This is the number of times the time record
% % % should be filtered.
% % % default is num_x_filter=2;
% %
% % N=3; % is the number of frequency bands per octave.
% % % Can be any number > 0.
% % % Default is 3 for third octave bands.
% %
% % min_f=20; % is the minimum frequency band to calculate (Hz).
% % % Must be graeater than 0.
% % % default is 20;
% %
% % max_f=20000; % max_f is the maximum frequency band to calculate
% % % (Hz). Must be graeater than 0.
% % % default is 20000;
% %
% % sensor=1; % Constant integer input for selecting the sensor type
% % % 1 is for acoustic microphone Pref=20E-6 (Pa)
% % %
% % % 2 is for accelerometer output is in same
% % % units as the input (m/s^2)
% % %
% % % 3 generic sensor multiply by 1: output is in same
% % % units as the input
% % %
% % % default is sensor=1; For a microphone
% %
% % settling_time=0.1; % (seconds) Time requiered for the filter to settle
% % % usually 0.1 seconds or less.
% % % This quantity is usually frequency dependent.
% %
% % 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 using 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
% %
% % fc_out % (Hz) array of center frequencies
% %
% % SP_levels % (dB)sound pressure levels for each mic channel
% % % and f or each frequency band
% %
% % SP_peak_levels % (dB) Maximum of the absolute value of the Peak
% % % levels and for each frequency band
% %
% % SP_bands % Time record for each mic channel and for each
% % % frequency band after filtering
% %
% %
% % **********************************************************************
% %
%
% Example='1';
%
% % Compare the spectra of white noise, pink noise, and brown noise.
% %
%
% x1 = spatialPattern([1,500000],0); % white noise has a linearly
% % increasing spectrum
%
% x2 = spatialPattern([1,500000],-1); % pink noise has a constant
% % spectrum
%
% x3 = spatialPattern([1,500000],-2); % brown noise has a linearly
% % increasing spectra
%
% Fs=50000; % (Hz) Sampling rate
%
% num_x_filter=2; % Number of times to filter the data. Minimum value is 1
% % typically a value of 2 to 10 at low
% % frequencies (Fc < 100), num_x_filter=10 has a
% % significant phase shift when using filter.
%
% N=3; % number of bands per octave.
%
% min_f=20; % is the minimum frequency band to calculate (Hz).
%
% max_f=20000; % is the maximum frequency band to calculate (Hz).
%
% sensor=1; % acoustic microphone
% % output is in dB
%
% settling_time=1; % (seconds) Time requiered for the filter to settle
% % usually 0.1 seconds or less.
% % This quantity is usually frequency dependent.
%
% filter_program=1; % 1 is for using the filter progam otherwise the
% % filtfilt program is used.
% % default is filter_program=1 using filter progam.
%
% resample_filter=1;
%
% [fc_out1, SP_levels1]=Nth_oct_time_filter(x1, Fs, num_x_filter, N, min_f, max_f, sensor, settling_time, filter_program, resample_filter);
% [fc_out2, SP_levels2]=Nth_oct_time_filter(x2, Fs, num_x_filter, N, min_f, max_f, sensor, settling_time, filter_program, resample_filter);
% [fc_out3, SP_levels3]=Nth_oct_time_filter(x3, Fs, num_x_filter, N, min_f, max_f, sensor, settling_time, filter_program, resample_filter);
%
% % Plot the results
% figure(1);
% semilogx(fc_out1, SP_levels1, 'color', [1 1 1], 'linewidth', 2, 'marker', 's', 'MarkerSize', 8);
% hold on;
% semilogx(fc_out2, SP_levels2, 'color', [1 0.6 0.784], 'linewidth', 2, 'linestyle', '--', 'marker', 'o', 'MarkerSize', 8);
% semilogx(fc_out3, SP_levels3, '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('Classical Third Octave Band Spectra', 'Fontsize', 40);
% set(gca, 'Fontsize', 20);
%
%
% % **********************************************************************
% %
% % References
% %
% % 1) ANSI S1.11-1986 American National Stadard Specification for
% % Octave-Band and Fractional-Octave-Band Analog
% % and Digital Filters.
% %
% %
% % **********************************************************************
% %
% % Subprograms
% %
% % This program requires the Matlab Signal Processing Toolbox
% % This program is based on the Octave Toolbox by Christophe Couvreur
% % Matlab Central File Exchange ID 69
% %
% %
% % List of Dependent Subprograms for
% % Nth_oct_time_filter
% %
% % 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) fastlts Peter J. Rousseeuw NA
% % 6) fastmcd Peter J. Rousseeuw NA
% % 7) filter_settling_data3 Edward L. Zechmann
% % 8) geospace Edward L. Zechmann
% % 9) match_height_and_slopes2 Edward L. Zechmann
% % 10) moving Aslak Grinsted 8251
% % 11) nth_freq_band 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) rmean Edward L. Zechmann
% % 17) rms_val Edward L. Zechmann
% % 18) sd_round Edward L. Zechmann
% % 19) sub_mean Edward L. Zechmann
% %
% %
% %
% % **********************************************************************
% %
% % Program was written by Edward L. Zechmann
% %
% % date 7 December 2008
% %
% % modified 8 December 2008 Updated Comments
% %
% % modified 10 December 2008 Updated Comments
% %
% % modified 16 December 2008 Generlaized program to Nth Octave Bands
% %
% % modified 22 December 2008 Updated Comments. Finished Upgrade
% %
% % modified 5 January 2009 Added sub_mean to remove running
% % average using a time constant at one-
% % half the lowest center frequency.
% %
% %
% % modified 26 March 2009 Changed progam into a wrapper for
% % Nth_oct_time_filter2
% %
% % modified 4 August 2010 Updated Comments
% %
% % modified 15 March 2012 Changed the range_size to 8 and the
% % downsample_factor to 20 to improve
% % accuaracy of low frueqnecies when
% % processing short time records.
% %
% % **********************************************************************
% %
% % Please feel free to modify this code.
% %
% % See Also: Nth_oct_time_filter2, Nth_octdsgn, nth_freq_band, octave, resample, ACdsgn, filter, filtfilt
% %
if (nargin < 1 || isempty(SP)) || ~isnumeric(SP)
SP=rand(1, 50000);
end
% Make the data have the correct data type and size
[SP]=convert_double(SP);
[num_mics, num_pts]=size(SP);
if num_mics > num_pts
SP=SP';
[num_mics num_pts]=size(SP);
end
if (nargin < 2 || isempty(Fs)) || ~isnumeric(Fs)
Fs=50000;
end
if (nargin < 3 || isempty(num_x_filter)) || ~isnumeric(num_x_filter)
num_x_filter=2;
end
if (nargin < 4 || (isempty(N)) || ~isnumeric(N))
N=20;
end
if (nargin < 5 || isempty(min_f)) || (logical(min_f < 0) || ~isnumeric(min_f))
min_f=20;
end
if (nargin < 6 || isempty(max_f)) || (logical(max_f < 0) || ~isnumeric(max_f))
max_f=20000;
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
if ~isequal(resample_filter, 2)
resample_filter=1;
end
% get the third octave band center frequencies
[fc] = nth_freq_band(N, min_f, max_f, 0);
% Call Nth_oct_time_filter2
[fc_out, SP_levels, SP_peak_levels, SP_bands]=Nth_oct_time_filter2(SP, Fs, num_x_filter, N, fc, sensor, settling_time, filter_program, resample_filter);
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