Advances in Music Technology with Deep Learning: From Music Analysis to Production.
Cum Laude
Supervisor: José Ramón Beltrán Blázquez.
Commitee: Isabel Barbancho, Antonio Miguel Artiaga, Iran R. Roman.
Musical Analysis with Artificial Intelligence.
ERASMUS: Politecnico di Milano
Musical Composition with a Neural Network
Honorary Distinction.
Viola
Supervisor: Dr. Elio Qinton
Music Restoration
Supervisor: Prof. Iran R. Roman
Microphone Array Upsampling for Acoustic Imaging
Supervisor: Dr. Taketo Akama
Symbolic Music Generation
Supervisor: Dr. Marc Delcroix
Target Sound Extraction. Accepted 2 papers: IWANENC 2024 and ICASSP 2025
Supervisor: Dr. Weihsiang Liao
Music Restoration with Diffusion Models. Accepted paper: ICASSP 2024
Symbolic Music Generation with Sequence Models
Build, train and test Deep Learning models with Pytorch for sound analysis and music generation and build APIs with Python Flask to develop them in production.
Audio Segmentation with CNNs
Design and perform sound productions
Drafting of procedures for Laboratory in mechanical tests. Performing measurements and tests with reports
Design of a support for a camera using Solid Edge and 3D printing
[1] LLMs in Music Composition. Data Skeptic Podcast, Aug 2023
[2] Music Composition with Deep Learning: A Review, AI Music Reading Group @ Mila x Vector Institute, Apr 2022 (Invited Talk)
University of Zaragoza
10 Directed and/or Co-directed Theses (B.S.C and Masters)
Pompeu Fabra University
1 Co-directed Master Thesis
University of Zaragoza
RTI-2018-096986-B-C31
AffectiveLab-T60-20R
OTRI 2022/0214: CARACTERIZACIÓN Y ANÁLISIS DE LAS SEÑALES ACÚSTICAS PRODUCIDAS EN LOS PROCESOS DE EBULLICIÓN Y DESBORDAMIENTO DE LÍQUIDOS
Journal and Conference Reviewing
TISMIR, TASLP, IJIMAI, CMJ, EURASIP, ICASSP
Conference Volunteer
International Society for Music Information Retrieval Conference (ISMIR)
Spanish (Mother Tongue)
English: C1 CAE
Italian: B2 PLIDA
French: B1 CERTACLES
Japanese: N5 JPLT
German: A1
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