8 citations · 16 across the 6 of their papers we have counts for
6 papers
SoundBeam meets M2D: Target Sound Extraction with Audio Foundation Model
Carlos Hernandez-Olivan, Marc Delcroix, Tsubasa Ochiai +4
Target sound extraction (TSE) consists of isolating a desired sound from a mixture of arbitrary sounds using clues to identify it. A TSE system requires solving two problems at onc…
A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives
Carlos Hernandez-Olivan, Javier Hernandez-Olivan, Jose R. Beltran
Music is one of the Gardner's intelligences in his theory of multiple intelligences. How humans perceive and understand music is still being studied and is crucial to develop artif…
musicaiz: A Python Library for Symbolic Music Generation, Analysis and Visualization
Carlos Hernandez-Olivan, Jose R. Beltran
In this article, we present musicaiz, an object-oriented library for analyzing, generating and evaluating symbolic music. The submodules of the package allow the user to create sym…
Subjective Evaluation of Deep Learning Models for Symbolic Music Composition
Carlos Hernandez-Olivan, Jorge Abadias Puyuelo, Jose R. Beltran
Deep learning models are typically evaluated to measure and compare their performance on a given task. The metrics that are commonly used to evaluate these models are standard metr…
Music Composition with Deep Learning: A Review
Carlos Hernandez-Olivan, Jose R. Beltran
Generating a complex work of art such as a musical composition requires exhibiting true creativity that depends on a variety of factors that are related to the hierarchy of musical…
Timbre Classification of Musical Instruments with a Deep Learning Multi-Head Attention-Based Model
Carlos Hernandez-Olivan, Jose R. Beltran
The aim of this work is to define a model based on deep learning that is able to identify different instrument timbres with as few parameters as possible. For this purpose, we have…