We work on the following research topics: sound and music description, music information retrieval, singing voice synthesis, audio source separation, music and audio processing.
Our current challenges are:
- Exploit the multimodal character of music to enhance its automatic processing.
- Reduce the semantic gap between automatic features and user descriptors through user-centered paradigms.
- Connect music description and music creation.
- Incorporate advanced learning techniques for music processing.
- Apply our current know-how on singing voice modelling to statistical-based modelling of animal vocalizations.
|Emilia Gómez, Head of lab||Pritish Chandna, PhD student|
|Jordi Bonada, Postdoc||Helena Cuesta, PhD student|
|Enric Guaus, Postdoc||Blai Meléndez Catalan, PhD student|
|Perfecto Herrera, Postdoc||Andrés Pérez, PhD student|
|Merlijn Blaauw, PhD student||Olga Slizovskaia, PhD student|
|Aggelos Gkiokas, Postdoc||Juan Gómez, PhD student|
|Felipe Navarro, Researcher||Lorenzo Porcaro, PhD student|
|Alba Guerrero, Collaborator||Furkan Yesiler, PhD student|
Within the area of Music Information Retrieval we aim at automatically generating “descriptors” that capture the sonological or musical features that are embedded in the audio signals. More details about our research in Music Information Retrieval.
Our research in the field of audio signal processing is wide and multidisciplinary, with an important focus on technology transfer acknowledged by dozens of patents and several commercial products of great success. Currently our interests spread in the area of singing voice synthesis, voice transformation, source separation and automatic soundscape generation. More details about our research in Voice and Audio Processing.