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followers: 10.0 following: 213.0 repos: 18.0 gists: 0.0

Name: Oriol Colomé Font

Type: User

Company: My Sheet Music Transcriptions

Location: Barcelona

Hi there 👋

I am a Catalan musician turned music scientist. I've spent 10+ years honing my craft across the music industry. From teaching to performing, engraving to arranging, producing to developing, my forte lies in blending tradition with MIR research.

Always eager to hit the following note, I'm ready for new challenges.

🔭 & 🌱 I’m currently learning and working on music similarity
💬 Ask me about anything that has to do with music!
📫 How to reach me: [email protected]
⚡ Fun fact: I recently discovered my new passion; Muay Thai 🇹🇭🙏

Oriol Colomé Font's Projects

arab-andal-motif-dev

Towards a systematic exploration of motif development in Arab-Andalusian Music

audio-signal-processing-for-music-applications

Assignments for Audio Signal Processing for Music Applications on MTG-UPF Msc Sound And Music Computing. Note: It's for my personal learning purpose.

cmc_smc

CMC Lecture Slides and Labs

hello-world

Just another repository

ismir2018_tutorial

master-thesis-latex

melody-extraction-in-symphonic-music-recordings

Algorithmic predominant melody extraction in in symphonic music recordings

mir_eval

Evaluation functions for music/audio information retrieval/signal processing algorithms.

mirdata

Python library for working with Music Information Retrieval datasets

mirdata-notebooks

Notebook examples using mirdata

musicboundariescnn

Code of the paper "Music Boundary Detection using Convolutional Neural Networks: A comparative analysis of combined input features" in Pytorch

oriolcolomefont

Github profile description

research-methods-mere

Meta-research analysis. Where is the spotlight shining on when it comes to inequality of opportunity in the research career?

salami-data-public

segment_hierarchy_labels

Investigating hierarchical music segmentations

sheetmidiretrieval

symbolic-soroll

The Sound of AI workshop on Generative Music

uncovering-high-level-content-in-the-time-domain

Leveraging self-supervised deep neural networks, inductive bias, and aural skills to learn deep audio embeddings with applications to boundary detection tasks.

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