About me
I am passionate about the use of modern technologies to learn, understand, create, and enjoy music.
I wrote my PhD dissertation on deep learning models for automatic Roman numeral analysis.
Nowadays, I work as a developer (and AI Researcher) for the Sibelius music notation software, within Avid Technology.
Interests
- Computational Music Theory
- Music Information Retrieval
- Deep Learning
- Music gamification
Education
PhD in Music Technology, 2022
McGill University, Montréal
MSc in Sound and Music Computing, 2017
Universitat Pompeu Fabra, Barcelona
Licenciatura en Informática, 2013
Universidad de Guadalajara, México
Peer-reviewed publications
(2023).
Musical Genre Recognition Based on Deep Descriptors of Harmony, Instrumentation, and Segments.
In EvoStar 2023.
(2022).
Challenging Sources: A New Dataset for OMR of Diverse 19th-Century Music Theory Examples.
In WoRMS 2022.
(2021).
AugmentedNet: A Roman Numeral Analysis Network with Synthetic Training Examples and Additional Tonal Tasks.
In ISMIR 2021.
(2020).
Data Quality Matters: Iterative Corrections on a Corpus of Mendelssohn String Quartets and Implications for MIR Analysis.
In ISMIR 2020.
(2020).
On Local Keys, Modulations, and Tonicizations.
In DLFM 2020.
(2020).
Harmalysis: A Language for the Annotation of Roman Numerals in Symbolic Music Representations.
In MEC.
(2019).
Key-Finding Based on a Hidden Markov Model and Key Profiles.
In DLFM 2019.
Other publications
(2021).
AugmentedNet: A Convolutional Recurrent Neural Network for Automatic Roman Numeral Analysis with Improved Data Augmentation.
In MML 2021.
(2020).
Harmonic Reductions As a Strategy for Creative Data Augmentation.
In ISMIR 2020.
(2020).
Do-Re-Myth: An Ear Training Game.
In COBS.
(2019).
The Effects of Translation Between Symbolic Music Formats: A Case Study with Humdrum, Lilypond, MEI, and MusicXML.
In MEC.
(2019).
Musical Tic-Tac-Toe.
In LMusTP 2019.
(2019).
Dandelot - Reading Music as a Game.
In LMUSTP 2019.