MusicTechnologyGroup
Research group specialised in audio signal processing, music information retrieval, musical interfaces, and computational musicology. The Music Technology Group (MTG) is part of the Department of Information and Communication Technologies at the Universitat Pompeu Fabra in Barcelona. The MTG wants to contribute to the improvement of the information and communication technologies related to sound and music, carrying out competitive research at the international level and at the same time transferring its results to society. To that goal, the MTG aims at finding a balance between basic and applied research while promoting interdisciplinary approaches that incorporate knowledge and methodologies from both scientific/technological and humanistic/artistic disciplines.
Mapping, measuring, and managing the harms of generative music
Algorithmic Auditing for Music Discoverability
Melodic and Metrical Elements of Expression in Hindustani Vocal Music
Ethical MIR Research for Trustworthy AI: Principles, Challenges, and Practices
Source separation without ground-truth data
A journey through generative music AI with Valerio Velardo
IA Colaborativa - El impacto de los datos en la IA Generativa
IA Abierta – Modelos para potenciar la creatividad musical
IA Ética – Responsabilidad y el futuro de la IA musical colaborativa
Analyzing Singing Voice Expressivity: Focus on Singing Voice Musical Dynamics
Music Identification with Audio Fingerprinting. An Industrial Perspective
Studying a Musical Repertoire with Computational Approaches: The Case of Carnatic Music
Design, Development, and Deployment of Real-Time Drum Accompaniment Systems
Embedded Machine Learning in Musical Instrument Design, by Chris Kiefer
David Toop and Sergi Jordà: open conversation on music and technology
Violin Performance Analysis using Weak Supervision
Deep Audio Representation Learning for Music Using Weak Supervision
High-Resolution Violin Transcription using Weak Labels
Sounds out of place? Score independent detection of conspicouous mistakes in piano performances.
TriAD: Capturing harmonics with 3D Convolutions
TapTamDrum: A Dataset for Dualized Drum Patterns
Efficient Supervised Training of Audio Transformers for Music Representation Learning
Leveraging pre-trained autoencoders for interpretable prototype learning of music audio
Critical art practices within Generative Music AI with Moisés Horta Valenzuela (𝔥𝔢𝔵𝔬𝔯𝔠𝔦𝔰𝔪𝔬𝔰)
Cátedra IA y Música – Presentation
Interpretable Deep-learning Models for Sound Event Detection and Classification
La IA y el ecosistema musical: desafíos, oportunidades y riesgos
Automatic Characterization and Generation of Music Loops and Samples for Electronic Music Production
Music Technology Group - Universitat Pompeu Fabra
Modeling Timbre for Neural Singing Synthesis