Elamur Research⌕
☰
home / authors / Oriol Nieto

Oriol Nieto

Oriol Nieto has 2 papers in our library, in 2024, most of them on Speech & spoken language, Reasoning, Language models & pretraining; often with Ashish Seth, Dinesh Manocha, Ramani Duraiswami.

2 papers in our library · 2024

OpenAlex · ORCID · h-index 18 · 64 works · 4,250 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 121 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7046

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢Adobe SystemsSan Jose · United States2022–2026 · 19 papersEmployment · Senior Audio Research Engineer, Audio Research Groupcurrent🎓New York UniversityNew York · United States2012–2018 · 13 papers🏢PandoraOakland · United States2017–2021 · 7 papers🎓Universitat Pompeu FabraBarcelona · Spain2008–2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 worksSignal Processing · 1 work

🧭 Topics 4Research subjects and languages the work is about.Reasoning · 2 worksSpeech & spoken language · 2 worksLanguage models & pretraining · 1 workEvaluation & benchmarks · 1 work

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities (2024 · 87 citations)
    Large audio-language models (LALMs) such as LTU, SALMONN and Pengi handle captioning and simple factual audio questions well.
  2. MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark (2024 · 67 citations)
    Large Audio-Language Models (LALMs) are usually tested on basic tasks such as ASR, acoustic scene classification, or genre labeling.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Mir_Eval: A Transparent Implementation Of Common Mir Metrics. ↗no summary yetColin Raffel et al.2014 · 282 citations
  2. GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning AbilitiesConference on Empirical Methods in Natural Language Processing (EMNLP)Sreyan Ghosh, Sonal Kumar, Ashish Seth, et al.2024 · 87 citations
  3. MMAU: A Massive Multi-Task Audio Understanding and Reasoning BenchmarkarXiv:2410.19168S Sakshi, Utkarsh Tyagi, Sonal Kumar, et al.2024 · 67 citations

 Co-authors here

7 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 121 times (of the 149,439 papers whose bibliographies we read); 35 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/oriol-nieto/