I plan on updating this section with general purpose scripts and also some machine learning package reviews. I am especially interested right now in FeatureTools, a package which automatically creates features based on existing data and specifications.
Script for Image Resolution Documentation (MATLAB)
(To be updated)
Script for RMS Standardization (MATLAB)
This MATLAB script (posted entirely below) was used to standardize auditory stimuli to have an identical average amplitude across the entirety of the auditory clip. It is also hosted on my GitHub.
% The code below takes audio wav files and normalizes them % to have an equal average loudness across time. % This script was used to normalize audio files for the manuscript % titled: "Towards a Better Understanding of In-Vehicle Auditory Warnings and Background Noise." % authors: Edin Šabić1, Jing Chen2, and Justin A. MacDonald1 % New Mexico State University (1), Old Dominion University (2) m = zeros(29,1); for wave_number = 1:29 % assumes that your files are named 1.wav, 2.wav, etc. % read in the sound file sound_in = audioread([int2str(1) '.wav']); sound_in = sound_in(:,1); % calculate the rms of the original wav file rms = sqrt(mean(sound_in.^2)); %rescale the sound so that the rms is 1 sound_in = sound_in ./ rms; % get the max sample value for the rescaled sound m(wave_number) = max(abs(sound_in)); % repeat this process for all of your other sound files end %this gives you the max sample across all of your stimuli max_sample = max(m); %rescale all 29 stimuli so that none of them have samples that exceed +/- 1 for wave_number = 1:29 % read in the sound file [sound_in,Fs] = audioread([int2str(wave_number) '.wav']); % calculate the rms of the original wav file rms = sqrt(mean(sound_in.^2)); %rescale the sound so that the rms is 1 sound_in = sound_in ./ rms; % rescale the sound 1 more time sound_in = sound_in ./ max_sample; audiowrite([int2str(wave_number) '_out.wav'],sound_in,Fs); end