georgid

AlignmentDuration

Lyrics-to-audio-alignement system. Based on Machine Learning Algorithms: Hidden Markov Models with Viterbi forced alignment. The alignment is explicitly aware of durations of musical notes. The phonetic model are classified with MLP Deep Neural Network.

AlignmentEvaluation

Scripts for computing common lyrics-to-audio alignment evaluation metrics. Usable evaluation for any token-based alignment (e.g. if token is word, phrase, note, section etc.) User for the evaluation of the MIREX Lyrics-to-audio challenge

chorus-vocal-covers

A heuristic approach to the detection of choruses in vocal cover versions. Done at the WiMIR workshop at ISMIR 2019 https://docs.google.com/presentation/d/1WYXxChgo8DI_NknyndPdcOuxlhAL_428Olg2fWaVy6U/edit#slide=id.g6f4f88d309_0_57

ENST-drums-dataset

the dataset used in the paper https://drive.google.com/file/d/0B4bIMgQlCAuqdGVRbVNNbzJfeUU/view

HMMDuration

Python Hidden Markov Models framework. Adapted for computationally optimal Viterbi forced alignment. Added Explicit Duration model

htkModelParser

Parses models created by the HTK Toolkit (http://htk.eng.cam.ac.uk/) as text files into Python class. It enables then various operations with the models like visualization and comparison.

lakh_vocal_segments_dataset

singing voice with annotations of vocal onsets, based on the matched MIDI from http://colinraffel.com/projects/lmd/

Lyrics2AudioAligner

lyrics-to-audio-alignement system. Initially done using HTK for rapid prototyping

pypYIN

python pYIN

vocal-detection

georgid/AlignmentDuration

Lyrics-to-audio-alignement system. Based on Machine Learning Algorithms: Hidden Markov Models with Viterbi forced alignment. The alignment is explicitly aware of durations of musical notes. The phonetic model are classified with MLP Deep Neural Network.

georgid/AlignmentEvaluation

Scripts for computing common lyrics-to-audio alignment evaluation metrics. Usable evaluation for any token-based alignment (e.g. if token is word, phrase, note, section etc.) User for the evaluation of the MIREX Lyrics-to-audio challenge

georgid/lakh_vocal_segments_dataset

singing voice with annotations of vocal onsets, based on the matched MIDI from http://colinraffel.com/projects/lmd/

georgid/Lyrics2AudioAligner

lyrics-to-audio-alignement system. Initially done using HTK for rapid prototyping

georgid/pypYIN

python pYIN

georgid/chorus-vocal-covers

A heuristic approach to the detection of choruses in vocal cover versions. Done at the WiMIR workshop at ISMIR 2019 https://docs.google.com/presentation/d/1WYXxChgo8DI_NknyndPdcOuxlhAL_428Olg2fWaVy6U/edit#slide=id.g6f4f88d309_0_57

georgid/otmm_vocal_segments_dataset

Manual annotations of audio segments that correspond to sections from score with singing voice present

georgid/SourceFilterContoursMelody

Melody extraction based on source-filter modelling

georgid/music_hack_sofia

Examples of extracting acoustic features with essentia

georgid/meowify

georgid/mfcc-htk-an-librosa

Reproduce the htk-type of MFCC features using the essentia framework. The MFCC extracted with essentia are compared to these extracted with htk these extracted with librosa

georgid/PhDThesis

The data needed to generate my phd thesis

georgid/Position-DBN-HMM-Lyrics

query by textual lyrics in audio with HHMM model of section positions using Viterbi

georgid/tune_puzzle

Tune Puzzle

georgid/align-magix

making a few web apps for testing

georgid/Curation_Users

georgid/docker

My Docker scripts and Dockerfile for several frameworks.

georgid/dunya

The Dunya music browser. Developed using Django 2

georgid/englishMLP2turkish

scripts to create mapping from English phoneme models as feed forward network multilayer perceptron network onto a GMM turksih phoneme model

georgid/essentia

C++ library of algorithms to extract features from audio files, including Python bindings.

georgid/IMFCC-visualization

Example of the inverse MFCC essentia feature

georgid/madmom

Python audio and music signal processing library. This is a fork adding support for synchronous tracking of vocal note onsets and metrical position in bar. The model used is Dynamic Bayesian Networks.

georgid/makam_acapella

acapella recordings of Makam

georgid/msaf

Music Structure Analysis Framework

georgid/pdnn

PDNN: A Python Toolkit for Deep Learning. http://www.cs.cmu.edu/~ymiao/pdnntk.html

georgid/publications_PhD

latex/lyx code and figures to reproduce the papers of my research http://mtg.upf.edu/biblio/author/810 that are the basis for my PhD http://compmusic.upf.edu/phd-thesis-georgi

georgid/searchByLyricsEval

evaluation scripts for search by lyrics (a.k.a. keyphrase spotting)

georgid/SINGmasterAndrioidWithGUI

sing master exrecise mode with complete GUI

georgid/turkish_makam_section_dataset

The section test dataset for classical Ottoman-Turkish makam music

georgid/your_first_neural_network