Miguel Civit

ORCID: 0000-0003-4310-6377
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About
Contact & Profiles
Research Areas
  • Neuroscience and Music Perception
  • Music Technology and Sound Studies
  • Natural Language Processing Techniques
  • Music and Audio Processing
  • Spanish Linguistics and Language Studies
  • Artificial Intelligence in Healthcare and Education
  • Online Learning and Analytics
  • Text Readability and Simplification
  • Topic Modeling
  • Modular Robots and Swarm Intelligence
  • IoT and Edge/Fog Computing
  • Emotion and Mood Recognition
  • Health, Education, and Physical Culture
  • Advertising and Communication Studies
  • COVID-19 diagnosis using AI
  • Engineering Education and Technology
  • Cinema History and Criticism
  • Diverse Interdisciplinary Research Innovations
  • Basque language and culture studies
  • Context-Aware Activity Recognition Systems

Universidad Loyola Andalucía
2024-2025

Universidad Loyola
2025

Universidad de Sevilla
2022-2024

Departamento de Educación
2024

Centre National de la Recherche Scientifique
2024

Université de Bourgogne
2024

Tomas Bata University in Zlín
2022

The SunoCaps dataset aims to provide an innovative contribution music data. Expert description of human-made musical pieces, from the widely used MusicCaps dataset, are as prompts for generating complete songs this dataset. This Automatic Music Generation is done with state-of-the-art Suno generator audio-based music. A subset 64 pieces currently included, a total 256 generated entries. stems four different variations each human piece; two versions based on original caption and aspect...

10.1016/j.dib.2024.110743 article EN cc-by Data in Brief 2024-07-18

Abstract Background Active Learning with AI‐tutoring in Higher Education tackles dropout rates. Objectives To investigate teaching‐learning methodologies preferred by students. AHP is used to evaluate a ChatGPT‐based studented learning methodology which compared another active and traditional methodology. Study Analytics alternatives, help students elect the best strategies according their preferences. Methods Comparative study of three counterbalanced Single‐Group 33 university It follows...

10.1111/exsy.13703 article EN cc-by Expert Systems 2024-08-21

Background: Active Learning with AI-tutoring in Higher Education tackles dropout rates. Objectives: To investigate teaching-learning methodologies preferred by students. ChatGPT-based gamified learning methodology is compared to another active and a traditional methodology. Study Analytics evaluate alternatives, their implementation, help students elect the best strategies according preferences. Methods: Comparative study of three Single-Group counterbalanced 45 university It follows...

10.22541/au.170995060.04086473/v1 preprint EN Authorea (Authorea) 2024-03-09

The SunoCaps dataset aims to provide an innovative contribution where expert description of human-made musical pieces, from the widely used MusicCaps dataset, are as promptsfor Automatic Music Generation with Suno state-of-the-art audio-based music generator. A subset 64 pieces is currently included, a total 256 entries. This stems generating four different variations for each human piece; two versions based on original caption and aspect description. As AI-generated also includes...

10.2139/ssrn.4832849 preprint EN 2024-01-01

This study aims to share a teaching experience sound design for audiovisual productions and compares different projects tackled by students. It is not intended be comparative analysis of types but rather an problems observed in profiles students the subject who it grades. The world audio can very interesting large part students, both those with creative technical inclinations. Musical creation production, synchronization images, dubbing, etc. They are disciplines that generally have high...

10.48550/arxiv.2408.02113 preprint EN arXiv (Cornell University) 2024-08-04
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