VocalMat.m

% ----------------------------------------------------------------------------------------------
% -- Title       : VocalMat
% -- Project     : VocalMat - A Tool for Automated Mouse Vocalization Detection and Classification
% ----------------------------------------------------------------------------------------------
% -- File        : VocalMat.m
% -- Group       : Dietrich Lab - Department of Comparative Medicine @ Yale University
% -- Standard    : <MATLAB 2018a>
% ----------------------------------------------------------------------------------------------
% -- Copyright (c) 2020 Dietrich Lab - Yale University
% ----------------------------------------------------------------------------------------------

clear all

% ----------------------------------------------------------------------------------------------
% -- USER DEFINED PARAMETERS
% ----------------------------------------------------------------------------------------------
% ----------------------------------------------------------------------------------------------
% -- VocalMat Identifier
% ----------------------------------------------------------------------------------------------
% -- save the output from the identifier, in case you only want to rerun the classifier
save_output_files = 0;
% -- max_interval: maximum allowed interval between points to be considered part of one vocalization
max_interval = 20;
% -- minimum_size: minimum number of points to be considered a vocalization
minimum_size = 6;
% ----------------------------------------------------------------------------------------------
% -- VocalMat Classifier
% ----------------------------------------------------------------------------------------------
% -- 0 = off; 1 = on.
save_plot_spectrograms    = 0; % plots the spectograms with axis
save_excel_file           = 1; % save output excel file with vocalization stats
scatter_step              = 3; % plot every third point overlapping the vocalization (segmentation)
axes_dots                 = 1; % show the dots overlapping the vocalization (segmentation)
bin_size                  = 300; % in seconds

disp('[vocalmat]: starting VocalMat.')
% -- add paths to matlab and setup for later use
root_path       = fileparts(mfilename('fullpath')); %Set this path to VocalMat's root folder
identifier_path = fullfile(root_path, 'vocalmat_identifier');
classifier_path = fullfile(root_path, 'vocalmat_classifier');
analysis_path = fullfile(root_path, 'vocalmat_analysis');
addpath(genpath(root_path));

try
    % -- check for updates
    vocalmat_github_version = strsplit(webread('https://github.com/ahof1704/VocalMat/raw/master/README.md'));
    vocalmat_github_version = vocalmat_github_version{end-2};
    vocalmat_local_version  = strsplit(fscanf(fopen(fullfile(root_path,'README.md'), 'r'), '%c'));
    vocalmat_local_version  = vocalmat_local_version{end-2};
    if ~strcmp(vocalmat_local_version, vocalmat_github_version)    

        opts.Interpreter = 'tex';
        opts.Default     = 'Continue';
        btnAnswer        = questdlg('There is a new version of VocalMat available. For more information, visit github.com/ahof1704/VocalMat', ...
                                    'New Version of VocalMat Available', ...
                                    'Continue', 'Exit', opts);
        switch btnAnswer
            case 'Exit'
                error('[vocalmat]: there is a new version of VocalMat available. For more information, visit our <a href="https://github.com/ahof1704/VocalMat/tree/master">GitHub page</a>. ') 
            case 'Continue'
                warning('[vocalmat]: there is a new version of VocalMat available. For more information, visit our <a href="https://github.com/ahof1704/VocalMat/tree/master">GitHub page</a>. ') 
        end
    end
catch
    % -- could not check for updates
    disp('[vocalmat]: checking for updates failed. Verify if you have internet connection.');
end

% -- check dependencies
disp('[vocalmat]: verifying MATLAB toolboxes needed to run');

try
    verLessThan('signal','1 ');
catch
    error('[vocalmat]: please download the Signal Processing Toolbox')
end

try
    verLessThan('images','1');
catch
    error('[vocalmat]: please download the Image Processing Toolbox')
end

try
    verLessThan('stats','1');
catch
    error('[vocalmat]: please download the Statistics and Machine Learning Toolbox')
end

try
    verLessThan('nnet','1');
catch
    error('[vocalmat]: please download the Deep Learning Toolbox')
end

% -- handle execution over to the identifier
disp(['[vocalmat]: starting VocalMat Identifier...'])
cd(fullfile(root_path, 'audios')); run('vocalmat_identifier.m')

% -- handle execution over to the classifier
disp(['[vocalmat]: starting VocalMat Classifier...'])
cd(classifier_path); run('vocalmat_classifier.m')

% -- handle execution over to the diffusion maps
disp(['[vocalmat]: starting VocalMat Analsyis...'])
sigma=0.5;
t=2; % diffusion coefficient
m=3; % dimension of embedded space
plot_diff_maps=1; % 1: plot embedding, 0: do not plot 
cd(analysis_path); run('diffusion_maps.m')

% -- (optional) handle execution over to the diffusion maps and performs alignment for
% two groups defined by the variable 'keyword'
% work_dir = 'path_to_folder_with_groups_to_be_compared'; 
% keyword{1} = 'Control'; keyword{2} = 'Treatment'; % tags for the groups
% cd(analysis_path); run('kernel_alignment.m')

close all
disp(['[vocalmat]: finished!'])