Oriol Nieto
Senior Research Engineer II at Adobe
Oakland, California, United States
Summary
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Senior🎓
Top SchoolOriol Nieto is a Senior Research Engineer II at Adobe Research with 13 years of experience building human-centered AI systems for audio creativity, from music and audiobooks to video and environmental sound. He blends deep expertise in music information retrieval, large-scale recommender systems, NLP, and audio signal processing with a strong focus on deep learning architectures and production deployment. Previously a Staff Scientist at Pandora/SiriusXM, he helped design music recommendation pipelines used by millions of listeners. An active open-source maintainer, Oriol has contributed core features and evaluation tools to widely used libraries like librosa and mir_eval, helping standardize how audio algorithms are measured. Trained as a PhD in Music Technology and with advanced study at Stanford, he pairs rigorous academic research with practical engineering—and in his spare time he still performs on guitar, violin and cajón.
13 years of coding experience
11 years of employment as a software developer
PhD Music Technology, PhD Music Technology at New York UniversityMaster Information Communication and Audiovisual Media Technologies, Master Information Communication and Audiovisual Media Technologies at Universitat Pompeu FabraUPC Universitat Politècnica de CatalunyaMA Music Science and Technology, MA Music Science and Technology at Stanford University
English, Catalan, Spanish, French
Stackoverflow
5,459reputation
2.3mreached
28answers
17questions
jupyter-notebook
top-5%
matplotlib
top-5%
audio
top-1%
ipython
top-5%
python
top-5%
Github Skills (21)
unit-testing10
dspic10
python10
numpy10
audio10
librosa10
music10
dspace10
data-analysis10
matplotlib9
scipy9
ipython9
jupyter-notebook9
macos6
python-dateutil6
Programming languages (10)
Github contributions (5)
mir-evaluation/mir_eval
Mar 2014 - Oct 2015
Evaluation functions for music/audio information retrieval/signal processing algorithms.
Role in this project:
Data Scientist
Data ScientistContributions:1 review, 31 commits, 3 PRs in 1 year 8 months
Contributions summary:Oriol primarily contributed to the evaluation of pattern discovery algorithms within the music information retrieval domain. Their work involved implementing and testing various metrics, including standard, establishment, and three-layer F-measures, alongside precision and recall calculations. The user also added functionality to load and process pattern data, created and tested unit tests for the implemented metrics. Furthermore, they extended the codebase with the inclusion of first-five pattern metrics and documented the project through extensive docstrings.
signalmlsevaluationaudiosignal-processing
librosa/librosa
Mar 2015 - Mar 2015
Python library for audio and music analysis
Role in this project:Data Scientist
Data ScientistContributions:13 commits, 1 PR, 10 comments in 2 days
Contributions summary:Oriol primarily contributed to the implementation and testing of the `tonnetz` feature within the `librosa` library. Their work involved adding the `chroma_to_tonnetz` function, vectorizing its computations for efficiency, and refactoring the code by moving the function to `librosa.feature.spectral`. The user added comprehensive unit tests and updated the docstrings to include examples and cite the relevant academic paper.
python-librarydtwpythonlibrosaaudio
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