Extracting and identifying relevant perceptual and musical features from audio is an essential step in the analysis and understanding of music signals. Our current research on audio feature analysis covers: pitch analysis, harmonic analysis, tonality analysis, onset analysis, dynamics analysis, timbre analysis, and audio problems detection.

References: Tamer et al. 2023; Perez et al. 2022; Narang et al. 2022; Plaja-Roglans et al. 2022;  H. Cuesta, 2022; Won et al. 2020; Alonso-Jiménez et al. 2019; Essentia algorithms.

With sound and music representation learning, we aim to develop audio features suitable for various tasks rather than specific to a training objective. We consider approaches that leverage large-scale audio and metadata collections to learn embeddings that are useful for applications in music classification, recommendation, sound event recognition, etc.

References: Alonso Jiménez et al. 2023a; Alonso Jiménez et al. 2023b; Zinemanas 2023; Alonso-Jimenez et al. 2022; Ferraro et al. 2021; Fonseca et al. 2021; Oramas 2017; Essentia models.

Within machine listening we focus on topics related to sound event classification, sound scene analysis, and automatic sound retrieval.

References: Weck et al. 2023; Weck et al. 2022; Weck et al. 2021;  E. Fonseca, 2021; X. Favory, 2021; Zinemanas et al. 2021.

Music is much more than audio recordings, it is multimodal and therefore it can be studied through many types of signals. Related with this multimodality, we do research on topics such as: audio to score alignment, automatic audio captioning, musical gesture analysis, and music score analysis.

References: Weck et al. 2023; Zinemanas et al. 2023; Ramoneda et al. 2022; Weck et al. 2022; Ramoneda et al. 2022; Morsi et al. 2022; M. Won 2022; Weck et al. 2021; Favory et al. 2021; D. Cabrera Dalmazo, 2020; A.Morsi, 2020.

In the last few years there has been an incredible advancement on the topic of audio source separation. In our research we take different approaches depending on the application being targeted and considering specific trade-offs.

References: Plaja-Roglans et al. 2023; Chanda et al. 2022; P. Chandna, 2021; O. Slizovskaia, 2021; Lluis et al. 2019; M. Miron, 2017.

Building on our extensive experience working on a variety of sound synthesis methodologies we are currently focusing on deep learning approaches to synthesize the singing voice and other musical instruments, such as drums.

References: M. Blaauw 2022; Ramires et al. 2022; Bonada et al. 2021; Blaauw et al. 2020; Ramires et al. 2020.

Creating music is a very complex process that current Artificial Intelligence approaches are trying to address. Related to this, we do research on symbolic music generation and on intelligent instruments.

References: Haki et al. 2022; Nuttall et al. 2021; Gómez et al. 2020; Haki et al. 2020.

The classification of musical signals is a major research topic within the field of music information retrieval, which supports many music applications currently being developed. We have been working on topics such as auto-tagging, mood classification, genre classification, instrument recognition, and representation learning.

References: Alonso-Jimenez et al. 2023; Buisson et al. 2022; Drysdale et al. 2022; Won et al. 2021; Gómez-Cañón et al., 2021; Won et al. 2020; O. Slizovskaia, 2020; J. Pons, 2019; Essentia models.

Music retrieval based on the analysis of audio signals relates to many specific research problems. In particular we have been working on: music version identification, music recommendation, search, and exploration, music metadata, and algorithmic biases.

References: Batlle-Roca et al. 2023; Schedl et al. 2022; L. Porcaro, 2022; P. Tovstogan 2022; F. Yesiler, 2022; A. Ferraro, 2021.

Musicological research has been slowly incorporating computational methodologies. Specially significant for us is the research based on processing large corpora of music data. For example,  we have been doing research on: style specific music analysis, musical pattern analysis, melodic analysis, harmonic analysis, rhythm analysis, structural analysis, and music metadata analysis.

References: Nuttall et al. 2023; Plaja-Roglans et al. 2023; Srinivasamurthy et al. 2023; Nuttall et al. 2022; R. Caro Repetto, 2018; Bogdanov et al. 2017; CompMusic.

The study of musical performances is especially relevant in the context of music education. Within that context we have been doing research on automatic performance assessment, score difficulty analysis, and music transcription.

References: Tamer et al 2023; Morsi et al. 2023; Ramoneda et al. 2023; Kim et al. 2023; Kim et al. 2022; Ramoneda et al. 2022; Ramoneda et al. 2022; Eremenko et al. 2020; MusicCritic demos.

Adequate corpora and datasets are the basis for doing data-driven research and creating machine learning models. Over many years we have been creating large corpora for tackling broad sound and music research tasks and also we have been developing datasets for creating specific machine learning models. Related to these, it has been important the development of evaluation methodologies and of annotation protocols.

References: Narang et al. 2023; Manco et al., 2023; Cortès et al. 2022; Bogdanov et al. 2022; Tamer et al. 2022; Srinivasamurthy et al. 2021; Fonseca et al. 2020; Ramires et al. 2020; Freesound; Open corpora and datasets.

Our research involves the development of efficient, reliable and robust software implementations and tools. For example, we have been working with Web audio, on efficient implementations of deep learning algorithms, on real-time implementations, and on complete MLOps systems.

References: Anthon et al. 2023; Marcos-Fernández et al. 2022; Correya et al. 2022; Correya et al. 2021; Ramires et al. 2020; Zinemanas et al. 2020; Alonso-Jiménez et al. 2020; Alonso-Jiménez et al. 2020; Essentia.js; Open software.

Musical interfaces are needed to create music and a number of research topics focus on them. For example we work on topics such as: tangible interfaces, brain interfaces, hardware interfaces, and adaptive digital music instruments.

References: A. Hugues et al. 2021; F. Font, 2021; S. Jordà, 2017; Z. Vamvakousis et al. 2016

Music is known to have the power to induce strong emotions and can effectively impact the health and well-being of individuals. Music interventions have started to incorporate music technology for improving different conditions, e.g. emotional disorders, autism, cancer, Alzheimer, and stroke rehabilitation.

References: Ramirez et al. 2023; Ramirez et al. 2018; Ramirez et al. 2015.