Name: Marcos da Silva Sampaio
Type: User
Company: Universidade Federal da Bahia
Bio: Professor of Music Theory and Composition at the Federal University of Bahia, Interested in Music Analysis supported by computers and Music Contour Relations.
Location: Salvador, BA. Brazil
Blog: https://marcos.sampaio.me
Marcos da Silva Sampaio's Projects
Annotated Beethoven Corpus (ABC)
371 Four-part Chorales by J.S. Bach in the Humdrum file format.
Digital edition of L. van Beethoven's string quartests in the Humdrum file format.
Decided to move the gist into a repo of it's own so I can better link to the files
A collection of simple graphics made with D3.js
A Django app for managing scientific publications.
An Emacs configuration bundle with batteries included
Estudo do repertório de flauta solo
Documentos miscelaneos do genos
Arquivos comuns de LaTeX, Make, etc
Goiaba, a software to process contour
Informações sobre harpa
Lightweight home server based on microservices, usable as desktop workstation
Port of the creative portfolio theme to Hugo
The Humdrum Toolkit: Analysis tools for music research
Humdrum corpora without tools
Digital edition of Joseph Haydn's string quartets in the Humdrum file format.
Digital edition of W.A. Mozart's string quartets in the Humdrum file format.
Collection of command-line programs for music analysis (Humdrum Toolkit & Humdrum Extras)
C++ programs and library for processing Humdrum data files. Best to install from https://github.com/humdrum-tools/humdrum-tools . See https://github.com/craigsapp/humlib for modernized Humdrum file parsing library.
A script to handle Lilypond musical scores and parts
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data
Personal website
Haydn data for analysis
Toolkit for Computer-Aided Musicology and Musical Analysis
Demonstrations for music21
Javascript port of music21 -- Toolkit for Computational Musicology
MusiContour: musical contour relations calculator
Python library to handle integer partitions and compute partitional analysis metrics.