You can also find all these publications on my Zenodo directory.
All the code that I can publish is available in my Framasoft Gitlab page.
A Convolutional Approach to Melody Line Identification in Symbolic Scores
Publication date: 2019-11-04 - Type: conferencepaper - [Download (not available)]() - [BibTex (not available)]() - [DOI (not available]()
Have you ever tried to understand what is the melody in a music score? Even if it seems easy for us, its definition is difficutl to formalize under strict mathemathical terms, and, thus, it is a complex task for a computer. We show a new state-of-art method adopting convolutional neural network to tackle this challenge: can a computer automatically identify the melody in a music score? See code and demos at: github website
Multimodal Music Information Processing and Retrieval: Survey and Future Challenges
This is an exhaustive review of the literature exploiting multiple sources of information to create amazing applications. Here, you can discover how heterogeneous aspects of music information have been used for typical music information retrieval tasks. This is the very first definition of the field “Multimodal Music Information Retrieval”. We show that many works (more than 80 papers) have already approached the field and that the multimodal approach can really improve upon standard approaches. We also list the future challenges and the methods that must still be tested in this promising field.
Symbolic Music Similarity Through a Graph-Based Representation
We describe a new method to represent music information at the symbolic level (such as music scores). We test it in a few retrieval tasks, showing that it has some benefits for a generic stylistic representation. It is able to represent music both in its contrapuntal and harmonic texture. It paves new ways in automatic music composition, music information retrieval and any task which deals with music scores (music transcription, optical music recognition, audio-to-score alignment, etc).
Enhanced Wikifonia Leadsheet Dataset
EWLD (Enhanced Wikifonia Leadsheet Dataset) is a music leadsheet dataset with more than 5.000 scores that comes with a lot of metadata about composers, works, lyrics and features. It is designed for musicological and research purposes.
A Public Domain version, named OpenEWLD, is available at https://framagit.org/sapo/OpenEWLD.
You can find an in-deep discussion in my Master Thesis.
Please, use the following paper as reference:
Simonetta, Federico, Carnovalini, Filippo, Orio, Nicola, & Rodà, Antonio. (2018). Symbolic Music Similarity Through a Graph-Based Representation. In Proceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion (pp. 26:1–26:7). New York, NY, USA: ACM. http://doi.org/10.1145/3243274.3243301 Zenodo link: https://zenodo.org/record/2537059
My Master Thesis:
F. Simonetta, “Graph based representation of the music symbolic level. A music information retrieval application”, Università di Padova, 2018. Zenodo link: https://zenodo.org/record/1476564
Graph based representation of the music symbolic level. A music information retrieval application
Modellizzazione musicale tramite catene di Markov