plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | vigente/gerardus-master | transfdiffreg.m | .m | gerardus-master/matlab/RegistrationToolbox/transfdiffreg.m | 33,512 | utf_8 | 0f37b315dc00fb417839b986a6100c1b | function [ttot, info, infoTransfdiff, imout] = transfdiffreg(transform, im, optReg, optDiff)
% TRANSFDIFFREG Transform diffusion registration of a sequence of images.
%
% TRANSFDIFFREG implements a registration algorithm for a sequence of
% images I = 1, 2, ..., N, such that each image I is aligned to its two
% neighbo... |
github | vigente/gerardus-master | transfdiff.m | .m | gerardus-master/matlab/RegistrationToolbox/transfdiff.m | 20,281 | utf_8 | 51daaf94c2f55fb062b2f9fec15d2711 | function [tout, info] = transfdiff(opt, tp, tm)
% TRANSFDIFF Transform diffusion algorithm for sequence of images.
%
% This function is part of a larger algorithm that solves the following
% registration problem:
%
% We have a sequence of images I=1,2,...,N. We want to register each image
% to its adjacent neighbours t... |
github | vigente/gerardus-master | regmatchedfilt.m | .m | gerardus-master/matlab/RegistrationToolbox/regmatchedfilt.m | 8,938 | utf_8 | df3893ae6ef4a3f40a7b6227bd772b28 | function [tElx, cmax, imm] = regmatchedfilt(imf, imm, alpha)
% REGMATCHEDFILT Matched filter registration for translation and rotation.
%
% REGMATCHEDFILT uses a matched filter approach to find a global optimum
% for the rigid (translation and rotation) registration of two images.
%
% Assuming white noise, if we have ... |
github | vigente/gerardus-master | cons_smacof_pip.m | .m | gerardus-master/matlab/PointsToolbox/cons_smacof_pip.m | 28,712 | utf_8 | f6f912cfd63e26e7ad78725da92946e3 | function [y, stopCondition, sigma, sigma0, t] ...
= cons_smacof_pip(dx, y, isFree, bnd, w, con, smacof_opts, scip_opts)
% CONS_SMACOF_PIP SMACOF algorithm with polynomial constraints (PIP file
% format).
%
% Scaling by MAjorizing a COnvex Function (SMACOF) is an iterative solution
% to the Multidimensional Scaling... |
github | vigente/gerardus-master | scimat_dmatrix_thickslice.m | .m | gerardus-master/matlab/PointsToolbox/scimat_dmatrix_thickslice.m | 5,455 | utf_8 | f76b39a9bd5829abba4479a397cd6417 | function [d, points] = scimat_dmatrix_thickslice(scimat, K)
% SCIMAT_DMATRIX_THICKSLICE Compute a distance/adjacency matrix for a
% segmentation that consists of scattered points in slices wide apart.
%
% [D, POINTS] = scimat_dmatrix_thickslice(SCIMAT, K)
%
% SCIMAT is a structure with the segmentation. The segmenta... |
github | vigente/gerardus-master | pts_simil_map_params.m | .m | gerardus-master/matlab/PointsToolbox/pts_simil_map_params.m | 5,201 | utf_8 | 3690f6e174961a9455b50c39f97e5d6e | function [rforms, rformh, rformt] = pts_simil_map_params(y, x)
% PTS_SIMIL_MAP_PARAMS Compute similarity transformation parameters
% between sets of points with unknown correspondence (Procrustes is used)
%
% [RFORMS, RFORMH, RFORMT] = PTS_SIMIL_MAP_PARAMS(Y, X)
%
% In their simplest form:
%
% Y: target point se... |
github | vigente/gerardus-master | thickslice_collate_sax_la.m | .m | gerardus-master/matlab/PointsToolbox/thickslice_collate_sax_la.m | 4,109 | utf_8 | 7d64071ed193392b6cd42fe17a72f51e | function [xsax, d] = thickslice_collate_sax_la(xsax, d, xla, K)
% THICKSLICE_COLLATE_SAX_LA Build a distance/adjacency matrix collating
% points from a Long Axis plane to a set of Short Axis planes
%
% [XOUT, DOUT] = thickslice_collate_sax_la(XSAX, DSAX, XLA, K)
%
% XSAX is a 3-column matrix where each row has the c... |
github | wilselby/MatlabQuadSimAP-master | rotateGFtoBF.m | .m | MatlabQuadSimAP-master/utilities/rotateGFtoBF.m | 832 | utf_8 | dbf7d0325005b9feae8054e026d0c3d3 | % Wil Selby
% Washington, DC
% May 30, 2015
% This function rotates a point or matrix of points from the Body Frame to
% the Global Frame based on the quadrotor's Euler angles (orientation)
function [X,Y,Z]=rotateGFtoBF(X,Y,Z,phi,theta,psi)
% define rotation matrix
R_roll = [...
1, 0, 0;...
... |
github | wilselby/MatlabQuadSimAP-master | position_PID.m | .m | MatlabQuadSimAP-master/utilities/position_PID.m | 2,912 | utf_8 | e5be4ab0c802d422ac2b32872a89c634 | % Wil Selby
% Washington, DC
% May 30, 2015
% This function implements a Proportional Integral Derivative Controller
% (PID) for the quadrotor. A high level controller outputs desired roll and
% pitch angles based on errors between the Global and desired X and Y
% positions. A lower level controller takes those... |
github | wilselby/MatlabQuadSimAP-master | quad_dynamics.m | .m | MatlabQuadSimAP-master/utilities/quad_dynamics.m | 3,132 | utf_8 | c0247beaf1e9fb2807a2b001d93ed58a | % Wil Selby
% Washington, DC
% May 30, 2015
% This function simulates the dynamics of the quadrotor. The inputs are the
% Euler angles and motor forces. The output of the function is the updated
% quadrotor linear accelerations in the Global Frame and the rotational
% accelerations in the Body Frame. See www.wi... |
github | wilselby/MatlabQuadSimAP-master | quad_dynamics_nonlinear.m | .m | MatlabQuadSimAP-master/utilities/quad_dynamics_nonlinear.m | 3,114 | utf_8 | 65320ff244bc85655407f860922d77f9 | % Wil Selby
% Washington, DC
% May 30, 2015
% This function simulates the dynamics of the quadrotor. The inputs are the
% Euler angles and motor forces. The output of the function is the updated
% quadrotor linear accelerations in the Global Frame and the rotational
% accelerations in the Body Frame. See www.wi... |
github | wilselby/MatlabQuadSimAP-master | anime.m | .m | MatlabQuadSimAP-master/utilities/anime.m | 2,029 | utf_8 | f594a50f249e4e6006e625960416d60a | % Wil and Madalyn
% Animation
% input [x,y,z,r,p,y
function anime()
clear all; close all; clc;
draw_quad();
end
function draw_quad(x,t)
persistent hFig;
% x = x(1);
% z = x(3);
% pitch = x(8);
x = 0;
z = 3;
pitch = pi/2;
f_fig_bound = 15;
r_fig_bound = -5;
t_fig_bound = 10;
b_fig_bound = -1;
base = ... |
github | wilselby/MatlabQuadSimAP-master | sensor_meas.m | .m | MatlabQuadSimAP-master/utilities/sensor_meas.m | 1,632 | utf_8 | 775243a17dd0e7990a019d841dd1693f | % Wil Selby
% Washington, DC
% July 5, 2015
% This function simulates sensor measurement noise for the GPS, barometer,
% and IMU. The noise variances come from each sensor's respective
% datasheet. See www.wilselby.com for more information.
function sensor_meas
global Quad;
%% GPS Measurements
if(mod(Q... |
github | wilselby/MatlabQuadSimAP-master | quad_motor_speed.m | .m | MatlabQuadSimAP-master/utilities/quad_motor_speed.m | 2,312 | utf_8 | ea66b2850e717a9ff0f5073958fcb213 | % Wil Selby
% Washington, DC
% May 30, 2015
% This function converts the desired force and moment control inputs into
% the desired speed of the motors. These speeds are then limited by the
% physical properties of our motor. The conventional control commands are
% then re-computed with the limited motor speeds... |
github | wilselby/MatlabQuadSimAP-master | rate_PID.m | .m | MatlabQuadSimAP-master/utilities/rate_PID.m | 2,107 | utf_8 | dc22062716f3afa14712208f9b64935f | % Wil Selby
% Washington, DC
% Sep 30, 2015
% This function implements a Proportional Integral Derivative Controller
% (PID) for the quadrotor. This is the lowest level controller. It recieved
% desired angular roll rates from the attitude controller. The outputs are
% then sent directly to the motors.
fun... |
github | wilselby/MatlabQuadSimAP-master | rotateBFtoGF.m | .m | MatlabQuadSimAP-master/utilities/rotateBFtoGF.m | 825 | utf_8 | 10d00984b21a7ef6b41b1babdef3e260 | % Wil Selby
% Washington, DC
% May 30, 2015
% This function rotates a point or matrix of points from the Body Frame to
% the Global Frame based on the quadrotor's Euler angles (orientation)
function [X,Y,Z]=rotateBFtoGF(X,Y,Z,phi,theta,psi)
% define rotation matrix
R_roll = [...
1, 0, 0;...
... |
github | wilselby/MatlabQuadSimAP-master | attitude_PID.m | .m | MatlabQuadSimAP-master/utilities/attitude_PID.m | 2,850 | utf_8 | 0adc49ce37b665a6e258ffa79b67e24a | % Wil Selby
% Washington, DC
% May 30, 2015
% This function implements a Proportional Integral Derivative Controller
% (PID) for the quadrotor. A lower level controller takes those inputs and
% controls the error between the deisred and actual Euler angles.
function attitude_PID
persistent z_error_sum;
... |
github | wilselby/MatlabQuadSimAP-master | quad_PID.m | .m | MatlabQuadSimAP-master/utilities/quad_PID.m | 2,920 | utf_8 | e973ba1473c11c639b0ff268d30d3ccc | % Wil Selby
% Washington, DC
% May 30, 2015
% This function implements a Proportional Integral Derivative Controller
% (PID) for the quadrotor. A high level controller outputs desired roll and
% pitch angles based on errors between the Global and desired X and Y
% positions. A lower level controller takes those... |
github | wilselby/MatlabQuadSimAP-master | init_plot.m | .m | MatlabQuadSimAP-master/utilities/init_plot.m | 4,193 | utf_8 | a3b1a2b51bc1fc3135a842457897a1ab | % Wil Selby
% Washington, DC
% May 30, 2015
% This function initializes the plots
function init_plot
% figure('units','normalized','position',[.1 .1 .8 .8],'name','Quadrotor AUS','numbertitle','off','color','w');
axes('units','normalized','position',[.2 .1 .6 .8]);
axis equal
% E1 = uicontrol('units',... |
github | mazoku/thesis-master | localized_seg.m | .m | thesis-master/localized_seg/localized_seg.m | 7,692 | utf_8 | bb2a7d2244c0d8449d484d59bf851478 | % Localized Region Based Active Contour Segmentation:
%
% seg = localized_seg(I,init_mask,max_its,rad,alpha,method)
%
% Inputs: I 2D image
% init_mask Initialization (1 = foreground, 0 = bg)
% max_its Number of iterations to run segmentation for
% rad (optional) Localizat... |
github | mazoku/thesis-master | sfm_local_chanvese.m | .m | thesis-master/sfm_chanvese_demo/sfm_local_chanvese.m | 2,085 | utf_8 | 9d7598e173efe6263bbd5e52d3af7226 | % [seg Lz] = sfm_local_chanvese(img,mask,iterations,lambda,rad,display)
%
% img - any image (2D or 3D). color images will be
% converted to grayscale.
%
% mask - binary image representing initialization.
% (1's foreground, 0's background)
%
% iterations - number of iterations to run
%
% lambda - rela... |
github | mazoku/thesis-master | fat_contour.m | .m | thesis-master/sfm_chanvese_demo/fat_contour.m | 548 | utf_8 | f6299ecd424a8148be65dce0ef6ba2bc | % FAT_CONTOUR draw a easily visible contour
function [h1 h2] = fat_contour(phi,dashed,c1)
%Coded by: Shawn Lankton
%Function: Display a contour
c2 = 'k';
if(~exist('c1','var')) c1 = 'r'; end
if(~exist('dashed','var')) dashed = false; end
t1 = 4;
t2 = 2;
hold on;
if(dashed)
h1 = contour(phi,[0 0],... |
github | redbKIT/redbKIT-master | test_all.m | .m | redbKIT-master/Problems/test_all.m | 7,235 | utf_8 | 535a58d2f452fb21b50f44339595a206 | function test_all
%TEST_ALL launch all the tests and create a log file
% This file is part of redbKIT.
% Copyright (c) 2015, Ecole Polytechnique Federale de Lausanne (EPFL)
% Author: Federico Negri <federico.negri at epfl.ch>
fid = fopen('test_log.txt','w');
TestFolder = pwd;
%% Write file header
t = now;
c... |
github | redbKIT/redbKIT-master | RBF_OfflineInterpolation.m | .m | redbKIT-master/RB_library/Tools/RBF_interpolation/RBF_OfflineInterpolation.m | 7,469 | utf_8 | b11bab7da5c1343fd5192bc0c186b14b | function [FOM] = RBF_OfflineInterpolation(FOM)
%RBF_OFFLINEINTERPOLATION offline construction of the RBF interpolant to
%the stability factor
%
% [FOM] = RBF_OFFLINEINTERPOLATION(FOM) requires as input a FOM struct
% containing the field stabFactor with the following MANDATORY FIELDS:
%
% - stabFactor.mu_in... |
github | redbKIT/redbKIT-master | POD_basis_computation.m | .m | redbKIT-master/RB_library/ReducedBasisMethod/POD_basis_computation.m | 1,632 | utf_8 | ad3b9728f786fc4d3baa6168f049a03b | function [V,Sigma,PSI] = POD_basis_computation(u, Xnorm, N_tol, D)
%POD_BASIS_COMPUTATION
%
% INPUT:
% u: snapshots matrix
% Xnorm: matrix norm
% N_tol: either number of basis to extract or some tolerance on
% the energy to capture
% D: quadrature weights norm
%
% OUTPUT... |
github | redbKIT/redbKIT-master | DEIM.m | .m | redbKIT-master/RB_library/HyperReduction/DEIM.m | 1,721 | utf_8 | 0c512f481e0ec0bb32f6262a07d72bfb | function [IDEIM, P, PHI] = DEIM(U, m)
%DISCRETEEMPIRICALINTERPOLATION performs DEIM algorithm
%
% [IDEIM, P, PHI] = DISCRETEEMPIRICALINTERPOLATION(U) given a matrix U
% of M column snapshot vectorsm, returns: (1) a vector IDEIM of length M
% containing the indices selected by DEIM, (2) a basis PHI = U, (3) a
%... |
github | redbKIT/redbKIT-master | DiscreteEmpiricalInterpolation.m | .m | redbKIT-master/RB_library/HyperReduction/DiscreteEmpiricalInterpolation.m | 2,192 | utf_8 | 2d102ab593f7c958d869bb59841bb9cc | function [IDEIM, PHI, P] = DiscreteEmpiricalInterpolation(U, m)
%DISCRETEEMPIRICALINTERPOLATION performs DEIM algorithm
%
% [IDEIM, PHI, P] = DISCRETEEMPIRICALINTERPOLATION(U) given a matrix U
% of M column snapshot vectorsm, returns: (1) a vector IDEIM of length M
% containing the indices selected by DEIM, (2)... |
github | redbKIT/redbKIT-master | ComputeSurfaceNormals3D.m | .m | redbKIT-master/FEM_library/Mesh/ComputeSurfaceNormals3D.m | 4,291 | utf_8 | d4d40f8496cfe442a95c78adce22899b | function [normalf, FaceToElem_list] = ComputeSurfaceNormals3D(boundaries, vertices, elements)
%ComputeSurfaceNormals3D computes normal vectors on the boundary vertices
%of a P1 TET mesh
%
% [normalf] = ComputeSurfaceNormals3D(boundaries, vertices, elements)
% This file is part of redbKIT.
% Author: Federico Neg... |
github | redbKIT/redbKIT-master | dataParser.m | .m | redbKIT-master/FEM_library/Tools/dataParser.m | 3,017 | utf_8 | 4644db3cd02ea21266ea97e7f2715286 | function [ DATA ] = dataParser( DATA )
%DATAPARSER input parser
% This file is part of redbKIT.
% Copyright (c) 2016, Ecole Polytechnique Federale de Lausanne (EPFL)
% Author: Federico Negri <federico.negri@epfl.ch>
DATA = parserLinearSolverOptions( DATA );
DATA = parserPreconditionerOptions( DATA );
DATA = p... |
github | redbKIT/redbKIT-master | exporter3dVTK_cell.m | .m | redbKIT-master/FEM_library/Tools/exporter3dVTK_cell.m | 4,795 | utf_8 | b467003d71937d86af2bfa89e139a580 | function exporter3dVTK_cell(data)
%
%*****************************************************************************
%% exporterSubDomains3dVTK writes a VTK file for unstructured mesh
%
% Description:
%
% VTK exporter for 3D UNSTRUCTURED FEM simulations.
% Use Paraview to plot results
% (adapted from the origin... |
github | redbKIT/redbKIT-master | exporter3dVTK.m | .m | redbKIT-master/FEM_library/Tools/exporter3dVTK.m | 4,663 | utf_8 | 668b9c80db27f3adc0af1e6058f59456 | function exporter3dVTK(data)
%EXPORTER3DVTK writes a VTK file for unstructured mesh
%
% Description:
%
% VTK exporter for 3D UNSTRUCTURED TETRAHEDRAL FEM simulations.
% Use Paraview to plot results (adapted from exporter2dVTK.m)
%
% Author:
%
% Matteo Astorino (ASCCI version)
% Federico Negri (BINARY vers... |
github | redbKIT/redbKIT-master | exporter2dVTK_cell.m | .m | redbKIT-master/FEM_library/Tools/exporter2dVTK_cell.m | 5,027 | utf_8 | 2a13a6b15a215176f9ff8ce8d5507b17 | function exporter2dVTK_cell(data)
%
%*****************************************************************************
%% exporter2dVTK writes a VTK file for unstructured mesh
%
% Description:
%
% VTK exporter for 2D UNSTRUCTURED FEM simulations.
% Use Paraview to plot results
% (adapted from the original work o... |
github | redbKIT/redbKIT-master | exporter2dVTK.m | .m | redbKIT-master/FEM_library/Tools/exporter2dVTK.m | 5,044 | utf_8 | 81b4880db91de52bcf6d6b516d1d54a2 | function exporter2dVTK(data)
%EXPORTER2DVTK writes a VTK file for unstructured mesh
%
% Description:
%
% VTK exporter for 2D TRIANGULAR UNSTRUCTURED FEM simulations.
% Use Paraview to plot results
% (adapted from the original work of John Burkardt)
%
% Author:
%
% Matteo Astorino (ASCII version... |
github | redbKIT/redbKIT-master | my_gmres.m | .m | redbKIT-master/FEM_library/LinearSolver/my_gmres.m | 24,830 | utf_8 | 5dee963b30ecb799b312ecc1dea7a90c | function [x,flag,relres,iter,resvec] = my_gmres(A,b,restart,tol,maxit,M1,M2,x,verbosity,varargin)
%MY_GMRES Generalized Minimum Residual Method.
%
% Slightly modified version of Matlab built-in GMRES function.
%
% X = GMRES(A,B) attempts to solve the system of linear equations A*X = B
% for X. The N-by-N coe... |
github | redbKIT/redbKIT-master | FSI_InterfaceMap.m | .m | redbKIT-master/FEM_library/Models/FSI/FSI_InterfaceMap.m | 3,345 | utf_8 | 12a9f07d86394545f7e859cc067760ab | function [MESH] = FSI_InterfaceMap(DATA, MESH)
%FSI_INTERFACEMAP preprocessing function for FSI solver
%
% [MESH] = FSI_INTERFACEMAP(DATA, MESH)
% Generates mappings from solid to fluid interface dofs and viceversa.
% This file is part of redbKIT.
% Copyright (c) 2016, Ecole Polytechnique Federale de Lausanne ... |
github | jckane/REAPER_GCI_evaluation-master | gci_sedreams.m | .m | REAPER_GCI_evaluation-master/lib/octave/gci_sedreams.m | 6,529 | utf_8 | 3ade975ff17ee7eeec3864bd29929185 | % SEDREAMS is a method for Glottal Closure Instant (GCI) determination.
%
% Octave compatible
%
% Description
% The Speech Event Detection based on the Residual Excitation And
% Mean-based Signal (SEDREAMS) is described in [1] and [2]. It acts in two
% successive steps. First short intervals where GCIs are ex... |
github | jckane/REAPER_GCI_evaluation-master | lpcresidual.m | .m | REAPER_GCI_evaluation-master/lib/octave/lpcresidual.m | 2,123 | utf_8 | 90e36140feb45a3d04df1f15c3241585 | % Function to derive the Linear Prediction residual signal
%
% Octave compatible
%
% Description
% Function to derive the Linear Prediction residual signal
%
% Inputs
% x : [samples] [Nx1] Input signal
% L : [samples] [1x1] window length (e.g., 25ms => 25/1000*fs)
% shift : [s... |
github | jckane/REAPER_GCI_evaluation-master | pitch_srh.m | .m | REAPER_GCI_evaluation-master/lib/octave/pitch_srh.m | 6,347 | utf_8 | 2abee172443f4d2da5bb7f0c38e0bfbe | % SRH is a robust pitch tracker.
%
% Octave compatible
%
% Description
% The Summation of the Residual Harmonics (SRH) method is described in [1].
% This algorithm exploits a criterion taking into the strength of the
% harmonics and subharmonics of the residual excitation signal in order to
% determine both... |
github | JohnFranchak/roi_coder-master | ROI.m | .m | roi_coder-master/util/ROI.m | 19,239 | utf_8 | 3069e61b4380fddc9c4f813746aebf87 | function varargout = ROI(varargin)
% ROI MATLAB code for ROI.fig
% ROI, by itself, creates a new ROI or raises the existing
% singleton*.
%
% H = ROI returns the handle to a new ROI or the handle to
% the existing singleton*.
%
% ROI('CALLBACK',hObject,eventData,handles,...) calls the local
% ... |
github | hxwang/revenue-prediction-master | histScore.m | .m | revenue-prediction-master/data/submission/histScore.m | 1,412 | utf_8 | 377281f60bdbfff80b2f343d3bac3e9d |
function histScore(filename, score, saveidx)
%load data
data = csvread(strcat('.\data\', filename, '.csv'), 1,1);
%Build Figure
figure1 = figure;
set(figure1,'units','normalized','outerposition',[0 0 1 1]);
axes1 = axes('Parent',figure1);
box(axes1,'on');
hold(axes1,'all');
set(axes1,'FontSize',30,'FontWeight','bo... |
github | thijor/NoiseTagging-master | pm_max_corr.m | .m | NoiseTagging-master/utilities/pm_max_corr.m | 2,096 | utf_8 | 43c9ed9bda9b87e2490c9842fe06308b | function [ ps, beta, cmaxsamples ] = pm_max_corr( cs, ndraws, estimate, alpha )
% [ps, beta] = pm_max_corr(cs, n, samples, estimate)
% Estimates the probability that the maximum correlation is larger than all
% the others. Takes absolute value of correlation and fits a beta
% distribution over the non-maximum correlati... |
github | thijor/NoiseTagging-master | jt_printmessage.m | .m | NoiseTagging-master/utilities/jt_printmessage.m | 3,303 | utf_8 | c0d1b021067037e521e5248f0cc81506 | function [Obj] = jt_printmessage(Obj,message,cnfcls)
%[Obj] = jt_printmessage(Obj,message)
%Prints message in a automatically down-scrolling panel.
%
% INPUT
% Obj = [struct] .fig : figure ([])
% .pan : panel
% .list: list with text
% message = [str] the message to ... |
github | thijor/NoiseTagging-master | jt_itr.m | .m | NoiseTagging-master/utilities/jt_itr.m | 1,226 | utf_8 | c78051e805597c2876e1e60c62ba895d | function B = jt_itr(N,P,T,method)
%itr = jt_itr(N,P,T,method)
%Computes the Information Transfer Rate defined by Wolpaw.
%
% INPUT
% N = [int] number of classes
% P = [flt] classification rate
% T = [flt] duration of one classification in seconds
%
% OPTIONS
% method = [str] method to compute itr (Wol... |
github | thijor/NoiseTagging-master | jt_correlation.m | .m | NoiseTagging-master/utilities/jt_correlation.m | 4,040 | utf_8 | cbcce2d72e8674e4d48897bd62ee48f4 | function [corrs,state] = jt_correlation(v,w,state,n)
%[corrs,state] = jt_correlation(v,w,state,n)
%
% INPUT
% v = [m p] new segment of m samples and p variables
% w = [m q] new segment of m samples and q variables
% state = [struct] structure with statistics, empty for first call ([])
% n ... |
github | thijor/NoiseTagging-master | jt_euclidean.m | .m | NoiseTagging-master/utilities/jt_euclidean.m | 2,012 | utf_8 | c476d56a46c22850035db14254d24726 | function [c] = jt_euclidean(v,w,a,l)
%[c] = jt_euclidean(v,w,action)
%(Cross-)Euclidean similarity
%
% INPUT
% v = [m p] matrix of p variables of m samples
% w = [m q] matrix of q variables of m samples
%
% OPTIONS
% a = [str] lock|shift|sgmfwd|sgmbck|sgmfwdbck (lock)
% l = [int] length of segment (100)
%
% OUT... |
github | thijor/NoiseTagging-master | zero_training_view.m | .m | NoiseTagging-master/utilities/zero_training_view.m | 6,683 | utf_8 | 9724f378ab8eb53f7eecb97655453900 | function zero_training_view( data, cfg )
%[ figs ] = zero_training_view(data, cfg)
% Shows results of zero training
%
% INPUT
% data = [struct]
% .response [n m] n responses of m length
% .spatial [n m] n responses of m length
% .act_res [n m] n activatio... |
github | thijor/NoiseTagging-master | jt_fit_sinc.m | .m | NoiseTagging-master/utilities/jt_fit_sinc.m | 474 | utf_8 | 728bd56397aa20feceebfe60908e05e6 | function yh = jt_fit_sinc(y)
y = y(:);
m = numel(y);
[~,i] = max(y);
y = circshift(y,floor(m/2 - i));
x = linspace(-pi,pi,m)';
p = fminsearch(@sincfit,[1 1 0],[],x,y);
yh = sincfun(x,p(1),p(2),p(3));
%plot(x,y,'-r',x,yh,'-k');
function sse = sincfit(coeff,x,y)
amp = coeff(1);
frq = coeff(2);
sft = coeff(... |
github | thijor/NoiseTagging-master | jt_cosine.m | .m | NoiseTagging-master/utilities/jt_cosine.m | 1,104 | utf_8 | 9c240ffe0831bab75fcd2362961c59c8 | function [c] = jt_cosine(v,w,action)
%[c] = jt_correlate(v,w,action)
%(Cross-)Cosine similarity
%
% INPUT
% v = [m p] matrix of p variables of m samples
% w = [m q] matrix of q variables of m samples
%
% OPTIONS
% action = [string] lock|sync|stop|shift|async (lock)
%
% OUTPUT
% c = [p*q p*q] all cross-correla... |
github | thijor/NoiseTagging-master | jt_tmc_apply.m | .m | NoiseTagging-master/tmc/jt_tmc_apply.m | 11,558 | utf_8 | 543495416af12b84b64e11e8af436e6d | function [labels,results,classifier] = jt_tmc_apply(classifier,X)
%[labels,results,classifier] = jt_tmc_apply(classifier,X)
%Apply the classifier to single-trial or multi-trial data.
%
% INPUT
% classifier = [struct] classifier structure
% X = [c m k] data of channels by samples by trials
%
% OUTP... |
github | thijor/NoiseTagging-master | jt_tmc_train.m | .m | NoiseTagging-master/tmc/jt_tmc_train.m | 23,426 | utf_8 | 4c383ca151795ed2ccc200eb7d1fae93 | function [classifier] = jt_tmc_train(data,cfg)
%[classifier] = jt_tmc_train(data,cfg)
%
% INPUT
% data = [struct] data structure:
% .X = [c m k] data of channels by samples by trials
% .y = [k 1] labels: one by trials
% .V = [s p] one period of trained sequences: samples by varia... |
github | thijor/NoiseTagging-master | jt_fit_transient_erp.m | .m | NoiseTagging-master/reconvolution/jt_fit_transient_erp.m | 1,653 | utf_8 | 769f7e642fd3a12efc87b67d758aa1ea | function [R] = jt_fit_transient_erp(R,L,cfg)
%[R] = jt_fit_transient_erp(E,L,cfg)
%Fits a model to a transient ERP.
%
% INPUT
% R = [c sum(L)] transient responses of [channels events]
% L = [1 e] length of each event
% cfg = [struct] configuration structure containing:
% .A = [flt] mean amplitude (1)
... |
github | thijor/NoiseTagging-master | jt_event_matrix.m | .m | NoiseTagging-master/reconvolution/jt_event_matrix.m | 3,944 | utf_8 | 167c0a8b56fdae1e7f6625d692d9afac | function [E,e] = jt_event_matrix(V,event)
%[E,ret] = jt_event_matrix(V,event)
%Creates a event matrix listing for each event whether it occurs at a
%particular point in time.
%
% INPUT
% V = [s n] bit-sequence of [samples instances]
% event = [str] type of event ('sequence')
% sequence : Eac... |
github | thijor/NoiseTagging-master | jt_make_kasami_code.m | .m | NoiseTagging-master/code/jt_make_kasami_code.m | 2,482 | utf_8 | d051bcc373b447742da139906f82adde | function [c,i] = jt_make_kasami_code(n,a,b)
%[c] = jt_make_kasami_code(m,a,b)
%Generates kasami codes.
%
% INPUT
% m = [int] register length (6)
% a = [1 p] array of p feedback tab points ([6 1])
% b = [1 q] array of q feedback tab points ([6 5 2 1])
%
% OUTPUT
% c = [2^m-1 (2^m+1)*2^(m/2)] bits by codes
% C... |
github | thijor/NoiseTagging-master | ms_testcase.m | .m | NoiseTagging-master/stimulation/ms_testcase.m | 3,822 | utf_8 | bb5328d49a37db9e8d36e5a23a738b15 | function [] = ms_testcase(cfg)
%[] = ms_testcase(cfg)
if nargin==1&&isnumeric(cfg)&&~isempty(cfg);
do_example(cfg);
return;
end
if nargin<1||isempty(cfg); cfg=[]; end
stage = jt_parse_cfg(cfg,'stage','test');
sentence = jt_parse_cfg(cfg,'sentence','TESTCASE MATRIXSPELLER');
codesfile = ... |
github | thijor/NoiseTagging-master | jt_mkTextureCircle.m | .m | NoiseTagging-master/stimulation/utilities/jt_mkTextureCircle.m | 4,390 | utf_8 | 914b9a690565bfba4a429c84e6aac98f | function [texels,srcRects,dstRects,imgs]=jt_mkTextureCircle(wPtr,symbols,varargin)
%[texels,srcRects,dstRects,imgs]=jt_mkTextureCircle(wPtr,symbols,varargin)
%
% INPUT
% wPtr = [int] window pointer
% symbols = {t s} symbols to display in t circles of s characters
%
% OPTIONS
% ViewPort = [1 4] part of ... |
github | thijor/NoiseTagging-master | save2pdf.m | .m | NoiseTagging-master/external/save2pdf.m | 2,197 | utf_8 | ad426d45bceaffe4a080e5b03f7e4a3f | %SAVE2PDF Saves a figure as a properly cropped pdf
%
% save2pdf(pdfFileName,handle,dpi)
%
% - pdfFileName: Destination to write the pdf to.
% - handle: (optional) Handle of the figure to write to a pdf. If
% omitted, the current figure is used. Note that handles
% are typically... |
github | thijor/NoiseTagging-master | aboxplot.m | .m | NoiseTagging-master/external/aboxplot.m | 9,277 | utf_8 | 71ae4054845d0464979070765ea18be5 | %
% Copyright (C) 2011-2012 Alex Bikfalvi
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 3 of the License, or (at
% your option) any later version.
% This program is distribu... |
github | thijor/NoiseTagging-master | strtokall.m | .m | NoiseTagging-master/external/strtokall.m | 1,183 | utf_8 | 6ed19c845b421cfec47bd706c794afab | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% The BrainStream software is free but copyrighted software, distributed %
% under the terms of the GNU General Public Licence as published by %
% the Free Software Foundation (either version 2, or at your option %
% any later vers... |
github | thijor/NoiseTagging-master | topoplot.m | .m | NoiseTagging-master/external/eeglab/topoplot.m | 69,764 | utf_8 | 40934d5dd26ad12938daacf2247deb0e | % topoplot() - plot a topographic map of a scalp data field in a 2-D circular view
% (looking down at the top of the head) using interpolation on a fine
% cartesian grid. Can also show specified channnel location(s), or return
% an interpolated value at an arbitrary scalp locat... |
github | thijor/NoiseTagging-master | readlocs.m | .m | NoiseTagging-master/external/eeglab/readlocs.m | 33,801 | utf_8 | 608323c832e6faa38cba919b70803656 | % readlocs() - read electrode location coordinates and other information from a file.
% Several standard file formats are supported. Users may also specify
% a custom column format. Defined format examples are given below
% (see File Formats).
% Usage:
% >> eloc = readlocs( ... |
github | thijor/NoiseTagging-master | convertlocs.m | .m | NoiseTagging-master/external/eeglab/convertlocs.m | 9,621 | utf_8 | 9455542daae2061f96f576b6dc3d46da | % convertlocs() - Convert electrode locations between coordinate systems
% using the EEG.chanlocs structure.
%
% Usage: >> newchans = convertlocs( EEG, 'command');
%
% Input:
% chanlocs - An EEGLAB EEG dataset OR a EEG.chanlocs channel locations structure
% 'command' - ['cart2topo'|'sph2topo'|'sphb... |
github | thijor/NoiseTagging-master | sph2topo.m | .m | NoiseTagging-master/external/eeglab/sph2topo.m | 3,339 | utf_8 | 44852faa1bc45208398680f08a50db29 | % sph2topo() - Convert from a 3-column headplot file in spherical coordinates
% to 3-column topoplot() locs file in polar (not cylindrical) coords.
% Used for topoplot() and other 2-D topographic plotting programs.
% Assumes a spherical coordinate system in which horizontal angles... |
github | thijor/NoiseTagging-master | setdiff_bc.m | .m | NoiseTagging-master/external/eeglab/setdiff_bc.m | 704 | utf_8 | 6ff610d1b3099610817bea2fc877650d | % setdiff_bc - setdiff backward compatible with Matlab versions prior to 2013a
function [C,IA] = setdiff_bc(A,B,varargin);
errorFlag = error_bc;
v = version;
indp = find(v == '.');
v = str2num(v(1:indp(2)-1));
if v > 7.19, v = floor(v) + rem(v,1)/10; end;
if nargin > 2
ind = strmatch('legacy', varargin);
if... |
github | thijor/NoiseTagging-master | union_bc.m | .m | NoiseTagging-master/external/eeglab/union_bc.m | 724 | utf_8 | 958b5b56de3e20c3892b2d7809e830fd | % union_bc - union backward compatible with Matlab versions prior to 2013a
function [C,IA,IB] = union_bc(A,B,varargin);
errorFlag = error_bc;
v = version;
indp = find(v == '.');
v = str2num(v(1:indp(2)-1));
if v > 7.19, v = floor(v) + rem(v,1)/10; end;
if nargin > 2
ind = strmatch('legacy', varargin);
if ~i... |
github | thijor/NoiseTagging-master | topo2sph.m | .m | NoiseTagging-master/external/eeglab/topo2sph.m | 3,809 | utf_8 | e3c2ecaa32b502b60d330735ade6b95d | % topo2sph() - convert a topoplot() style 2-D polar-coordinate
% channel locations file to a 3-D spherical-angle
% file for use with headplot()
% Usage:
% >> [c h] = topo2sph('eloc_file','eloc_outfile', method, unshrink);
% >> [c h] = topo2sph( topoarray, method, unshrink );
%
% Inputs:
%... |
github | thijor/NoiseTagging-master | intersect_bc.m | .m | NoiseTagging-master/external/eeglab/intersect_bc.m | 752 | utf_8 | 72bc774f899f5fb5e3b75d2c1ef99049 | % intersect_bc - intersect backward compatible with Matlab versions prior to 2013a
function [C,IA,IB] = intersect_bc(A,B,varargin);
errorFlag = error_bc;
v = version;
indp = find(v == '.');
v = str2num(v(1:indp(2)-1));
if v > 7.19, v = floor(v) + rem(v,1)/10; end;
if nargin > 2
ind = strmatch('legacy', varargin... |
github | thijor/NoiseTagging-master | loadtxt.m | .m | NoiseTagging-master/external/eeglab/loadtxt.m | 6,184 | utf_8 | ef03acb274df5eb7d8178df9f5d565bf | % loadtxt() - load ascii text file into numeric or cell arrays
%
% Usage:
% >> array = loadtxt( filename, 'key', 'val' ...);
%
% Inputs:
% filename - name of the input file
%
% Optional inputs
% 'skipline' - number of lines to skip {default:0}. If this number is
% negative the program will only sk... |
github | thijor/NoiseTagging-master | finputcheck.m | .m | NoiseTagging-master/external/eeglab/finputcheck.m | 9,133 | utf_8 | fe838fecdd60e76a4006a13c7c1b20e4 | % finputcheck() - check Matlab function {'key','value'} input argument pairs
%
% Usage: >> result = finputcheck( varargin, fieldlist );
% >> [result varargin] = finputcheck( varargin, fieldlist, ...
% callingfunc, mode, verbose );
% Input:
% varargin - Cell array ... |
github | owenbupt/particle-filter-master | fgreedy.m | .m | particle-filter-master/fgreedy.m | 1,800 | utf_8 | 012bf4833cde4cbc05fc8c82b4fc80f1 | %% fgreedy: the function form of the greedy algorthim
function [greedy_cost] = fgreedy(k_start, Achose, S_kpi, weight_kp, x_target, vx_target, y_target, vy_target, x_target_hat, vx_target_hat, y_target_hat, vy_target_hat)
%%
% Define the related parameters
% Information for the whole
% sampling interva... |
github | owenbupt/particle-filter-master | r.m | .m | particle-filter-master/r.m | 117 | utf_8 | be9aa323f684edde663b5879f63d3dec | %% r: caculate the distance between a and b
function [dista] = r(a, b, c, d)
dista = sqrt((a - c)^2 + (b - d)^2); |
github | carabiasjulio/SourceLocalization-master | srpphat.m | .m | SourceLocalization-master/srpphat.m | 3,943 | utf_8 | 34605fff9eca16512ec49215a15061a2 | function [finalpos,finalsrp]=srpphat(x, mic_loc, fs, lsb, usb)
%% This function uses SRP-PHAT
%% Inputs:
%%% 1) x is the multi-channel input data (samples x channels)
%%% 2) mic_loc is the microphone 3D-locations (M x 3) ( in meters)
%%% 3) fs: sampling rate (Hz)
%%% 4) lsb: a row-vector of the lower rectangular search... |
github | personalrobotics/moped-master | sfm_alignment_gui.m | .m | moped-master/moped2/modeling/sfm_alignment_gui.m | 23,461 | utf_8 | 2aa817bb74184ad10cc425ce7b6a9d5c | function varargout = sfm_alignment_gui(varargin)
% SFM_ALIGNMENT_GUI - Align a model with a predefined shape
%
% Usage: sfm_alignment_gui(model);
% sfm_alignment_gui(model, mesh);
%
% Input:
% model - SFM model you with to scale, rotate or translate
% mesh - Structure that contains mesh.x, me... |
github | personalrobotics/moped-master | getCameraPos.m | .m | moped-master/moped2/modeling/getCameraPos.m | 3,045 | utf_8 | b2f17045d9e4a26b93e7d290c8806cd3 | function cam_pose = getCameraPos(pts2D, pts3D, K, init_R, init_T)
% GETCAMERAPOS - Find camera position from a set of 2D-3D correspondences.
%
% Usage: getCameraPos(pts2D, pts3D, K, init_R, init_T);
%
% Input:
% pts2D - 2-by-N array of 2D positions (in pixels)
% pts3D - 3-by-N array of 3D positions (in world co... |
github | personalrobotics/moped-master | sift.m | .m | moped-master/moped2/modeling/sift.m | 2,606 | utf_8 | 17ee2c4e4870f31fca50f58f536b27af | % SIFT - This function reads an image and returns its SIFT keypoints.
%
% Usage: [image, descriptors, locs] = sift(imageFile)
%
% Input parameters:
% imageFile: the file name for the image.
%
% Returned:
% image: the image array in double format
% descriptors: a K-by-128 matrix, where each row gives a... |
github | personalrobotics/moped-master | sfm_export_xml.m | .m | moped-master/moped2/modeling/sfm_export_xml.m | 5,267 | utf_8 | f61795559ee740c7a13dbd4fe06ad34d | function sfm_export_xml (filename, model, full_export, wt_append)
% SFM_EXPORT_MODEL - Export SFM model to file in XML format
%
% Usage: sfm_export_xml(filename, model, full_export, 'w')
%
% Input:
% filename - Text file to write to.
% model - SFM model to be exported.
% full_export - Export EVERYTHING from a... |
github | personalrobotics/moped-master | sfm_bundler_book.m | .m | moped-master/moped2/modeling/sfm_bundler_book.m | 3,111 | utf_8 | d703df5f9b361980110b01ab174bac5f | function model = sfm_bundler_book(name, front_image_file, back_image_file, ...
spine_image_file, real_size, output_file)
% SFM_BUNDLER_BOOK - Create SFM model using 3 planar images
%
% Usage: model = sfm_bundler_book(name, front_image, back_iamge, spine_image,
% real_size, output_file)
%
% Input:
%... |
github | personalrobotics/moped-master | projectPts.m | .m | moped-master/moped2/modeling/projectPts.m | 3,394 | utf_8 | 5b421de587e2a4433f1475bfd5cca929 | function [pts2D in_front] = projectPts(varargin)
% PROJECTPTS - Use the perspective projection to map pts in 3D to 2D.
% Function to use in SFM to jointly optimize the camera poses and 3D
% points. If you are using this function along with Levenberg-Marquardt
% optimization, you will find the 'alternative usage' ... |
github | personalrobotics/moped-master | sfm_alignment_gui.m | .m | moped-master/moped3d/modeling/sfm_alignment_gui.m | 23,461 | utf_8 | 2aa817bb74184ad10cc425ce7b6a9d5c | function varargout = sfm_alignment_gui(varargin)
% SFM_ALIGNMENT_GUI - Align a model with a predefined shape
%
% Usage: sfm_alignment_gui(model);
% sfm_alignment_gui(model, mesh);
%
% Input:
% model - SFM model you with to scale, rotate or translate
% mesh - Structure that contains mesh.x, me... |
github | personalrobotics/moped-master | sfm_export_xml.m | .m | moped-master/moped3d/modeling/sfm_export_xml.m | 5,267 | utf_8 | f61795559ee740c7a13dbd4fe06ad34d | function sfm_export_xml (filename, model, full_export, wt_append)
% SFM_EXPORT_MODEL - Export SFM model to file in XML format
%
% Usage: sfm_export_xml(filename, model, full_export, 'w')
%
% Input:
% filename - Text file to write to.
% model - SFM model to be exported.
% full_export - Export EVERYTHING from a... |
github | personalrobotics/moped-master | projectPts.m | .m | moped-master/moped3d/modeling/projectPts.m | 3,394 | utf_8 | 5b421de587e2a4433f1475bfd5cca929 | function [pts2D in_front] = projectPts(varargin)
% PROJECTPTS - Use the perspective projection to map pts in 3D to 2D.
% Function to use in SFM to jointly optimize the camera poses and 3D
% points. If you are using this function along with Levenberg-Marquardt
% optimization, you will find the 'alternative usage' ... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | crossValidate.m | .m | Predominant_Instrument_Recognition-master/crossValidate.m | 949 | utf_8 | b92721cfcde04db6694164ccd6290bbb | % Evaluate the model using cross validation on the training set.
function [accuracy, confusion_matrix, time_seconds] = ...
crossValidate(mode, n_fold)
addpath('Whitening', 'Scanning', 'Preprocessing', 'Metrics', ...
'Feature_Extraction');
% Only use the first 20 data points from each class. To increase sp... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | createTrainingData.m | .m | Predominant_Instrument_Recognition-master/createTrainingData.m | 1,433 | utf_8 | 8fe7ec999d0971f48753dc94501e2df7 | % Reads the IRMAS dataset into an N x M matrix where rows are feature
% vectors and a corresponding N x 1 vector of labels. A convenience
% function that reads in audio data, preprocesses it, extracts features.
% Supports modes for baseline and novel approaches.
function [training_labels, training_features] = ...
... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | whiten.m | .m | Predominant_Instrument_Recognition-master/Whitening/whiten.m | 948 | utf_8 | bc2592a6a7590ebd5a1646e188aa7dba | % Whiten data by subtracting the mean and dividing by the standard
% deviation. Mean and standard deviation are computed from the training
% data but whitening is applied to both the trianing and the test data.
function [white_train, white_test] = whiten(train, test)
num_train_features = size(train, 2);
num_train_dat... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | extractFeatures.m | .m | Predominant_Instrument_Recognition-master/Feature_Extraction/extractFeatures.m | 346 | utf_8 | d2301108508321587cb4ad672f84dd5c | % Extract a feature vector from an audio signal.
function features = extractFeatures(audio, Fs, mode)
if (strcmp(mode, 'novel'))
% Extract features for the novel approach.
elseif (strcmp(mode, 'baseline'))
features = extractACAFeatures(audio, Fs);
else
disp('You must select either the baseline or nov... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | extractACAFeatures.m | .m | Predominant_Instrument_Recognition-master/Feature_Extraction/extractACAFeatures.m | 898 | utf_8 | 12e8349907eb990293eb6696a39c3ca3 | % Extract a feature vector from an audio signal.
function features = extractACAFeatures(audio, Fs, mode)
features = [];
ACA_FEATURES = ...
{'SpectralCentroid'; 'SpectralCrest'; 'SpectralDecrease'; ...
'SpectralFlatness'; 'SpectralFlux'; 'SpectralKurtosis'; ...
'SpectralMfccs'; 'SpectralPitchChroma'; 'Sp... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | yin2.m | .m | Predominant_Instrument_Recognition-master/Third_Party/yin/junk/yin2.m | 2,235 | utf_8 | b91a6e57061458dd6bd01fc6a7162a27 | function r=yin2(p,fileinfo)
% YIN2 - fundamental frequency estimator
% new version (feb 2003)
%
%
% process signal a chunk at a time
idx=0;
totalhops=round(fileinfo.nsamples / p.hop);
r1=nan*zeros(1,totalhops);r2=nan*zeros(1,totalhops);
r3=nan*zeros(1,totalhops);r4=nan*zeros(1,totalhops);
idx2=0+round(p.wsize/2/p.ho... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | yink.m | .m | Predominant_Instrument_Recognition-master/Third_Party/yin/private/yink.m | 3,423 | utf_8 | f307172b91326610c6be6e573c56124e | function r=yink(p,fileinfo)
% YINK - fundamental frequency estimator
% new version (feb 2003)
%
%
%global jj;
%jj=0;
% process signal a chunk at a time
idx=p.range(1)-1;
totalhops=round((p.range(2)-p.range(1)+1) / p.hop);
r1=nan*zeros(1,totalhops);r2=nan*zeros(1,totalhops);
r3=nan*zeros(1,totalhops);r4=nan*zeros(1,to... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | ComputePitch.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/ComputePitch.m | 3,073 | utf_8 | d9bd71be6bbc3b363071386d5cb0d9a6 | % ======================================================================
%> @brief computes the fundamental frequency of the (monophonic) audio
%>
%> supported pitch trackers are:
%> 'SpectralAcf',
%> 'SpectralHps',
%> 'TimeAcf',
%> 'TimeAmdf',
%> 'TimeAuditory',
%> 'TimeZeroCrossings',
%>
%> @param cP... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureSpectralMfccs.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureSpectralMfccs.m | 2,537 | utf_8 | 216bf6b48fc20f9d5aa8ec9326b85096 | % ======================================================================
%> @brief computes the MFCCs from the magnitude spectrum (see Slaney)
%> called by ::ComputeFeature
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data (unused)
%>
%> @retval vmfcc mel f... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureSpectralTonalPowerRatio.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureSpectralTonalPowerRatio.m | 1,084 | utf_8 | 04c58ef5bb8bae552051717d531dbbde | % ======================================================================
%> @brief computes the tonal power ratio from the magnitude spectrum
%> called by ::ComputeFeature
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data (unused)
%> @param G_T: energy thres... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureTimeMaxAcf.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureTimeMaxAcf.m | 1,802 | utf_8 | 63175904e01b5c20d802f47e319539dc | % ======================================================================
%> @brief computes the ACF maxima of a time domain signal
%> called by ::ComputeFeature
%>
%> @param x: audio signal
%> @param iBlockLength: block length in samples
%> @param iHopLength: hop length in samples
%> @param f_s: sample rate of a... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | ToolFreq2Midi.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/ToolFreq2Midi.m | 428 | utf_8 | bdc189b13b9e86fcabeb4e67d078afa4 | % ======================================================================
%> @brief converts frequency to MIDI pitch
%>
%> @param fInHz: frequency
%> @param f_A4: tuning frequency
%>
%> @retval p MIDI pitch
% ======================================================================
function [p] = ToolFreq2Midi(fInH... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | NoveltyLaroche.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/NoveltyLaroche.m | 587 | utf_8 | d0df56876bf8ca1410eee599463995c8 | % ======================================================================
%> @brief computes the novelty measure used by laroche
%> called by ::ComputeNoveltyFunction
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data (unused)
%>
%> @retval d_lar novelty meas... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureSpectralFlux.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureSpectralFlux.m | 614 | utf_8 | 4c8d269753c983d037741fe2a51cf6b3 | % ======================================================================
%> @brief computes the spectral flux from the magnitude spectrum
%> called by ::ComputeFeature
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data (unused)
%>
%> @retval v spectral flux
... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | NoveltyHainsworth.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/NoveltyHainsworth.m | 664 | utf_8 | 9b8b51e9dcb6d41162516d849011b4c4 | % ======================================================================
%> @brief computes the novelty measure used by Hainsworth
%> called by ::ComputeNoveltyFunction
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data (unused)
%>
%> @retval d_hai novelty m... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | PitchTimeAcf.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/PitchTimeAcf.m | 1,339 | utf_8 | c5c625592565dcaaa5eec7e8e14cd1a7 | % ======================================================================
%> @brief computes the lag of the autocorrelation function
%> called by ::ComputePitch
%>
%> @param x: audio signal
%> @param iBlockLength: block length in samples
%> @param iHopLength: hop length in samples
%> @param f_s: sample rate of au... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureSpectralSlope.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureSpectralSlope.m | 700 | utf_8 | 474c799b423ba2d9a37f530b85dcf8a2 | % ======================================================================
%> @brief computes the spectral slope from the magnitude spectrum
%> called by ::ComputeFeature
%>
%> @param X: spectrogram (dimension FFTLength X Observations)
%> @param f_s: sample rate of audio data (unused)
%>
%> @retval vsk spectral sl... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | ComputeNoveltyFunction.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/ComputeNoveltyFunction.m | 2,640 | utf_8 | 962dc7417eebb02ed7daa2f855a33cb1 | % ======================================================================
%> @brief computes the novelty function for onset detection
%>
%> supported novelty measures are:
%> 'Flux',
%> 'Laroche',
%> 'Hainsworth'
%>
%> @param cNoveltyName: name of the novelty measure
%> @param afAudioData: time domain sample... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureTimeStd.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureTimeStd.m | 1,085 | utf_8 | fbf0b3aa380fdf9ddebe367e8cf8de90 | % ======================================================================
%> @brief computes the standard deviation of a time domain signal
%> called by ::ComputeFeature
%>
%> @param x: audio signal
%> @param iBlockLength: block length in samples
%> @param iHopLength: hop length in samples
%> @param f_s: sample r... |
github | cplaguna-audio/Predominant_Instrument_Recognition-master | FeatureTimePredictivityRatio.m | .m | Predominant_Instrument_Recognition-master/Third_Party/ACA_Matlab/FeatureTimePredictivityRatio.m | 1,343 | utf_8 | 0e98e908aac422dd3f0db97a34f5846c | % ======================================================================
%> @brief computes the zero crossing rate from a time domain signal
%> called by ::ComputeFeature
%>
%> @param x: audio signal
%> @param iBlockLength: block length in samples
%> @param iHopLength: hop length in samples
%> @param f_s: sample... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.