MIP-Frontiers Final Workshop
The project New Frontiers in Music Information Processing (MIP-Frontiers), funded by Marie Skłodowska-Curie ITN program, presents its results
13.10.2021
MIP-Frontiers project (New Frontiers in Music Information Processing) has supported 15 PhD students at four universities working with a range of industry and cultural partners. This final workshop allows the fellows to present the main results of their research over the last three years. Invited researchers in the field will also participate with keynote presentations. The Music Technology Group is part of this project, with 3 PhD students participating: António Ramires, Philip Tovstogan, Furkan Yesiler.
Date: Thursday 15th - Friday 16th of October 2021
Time Zone: UTC+01 (UK time)
Location: Online (Zoom link)
Thursday 14th October 2021
| 9:00 | Welcome |
| Simon Dixon, Queen Mary University of London | |
| Domna Paschalidou, European Research Executive Agency | |
| 9:15 | Data-Driven Musical Version Identification: Accuracy, Scalability, and Bias Perspectives |
| Furkan Yesiler, Universitat Pompeu Fabra | |
| Deep Learning Methods for Musical Instrument Separation and Recognition | |
| Carlos Lordelo, Queen Mary University of London & DoReMIR | |
| 10:30 | Informed Audio Source Separation with Deep Learning in Limited Data Settings |
| Kilian Schulze-Forster, Telecom Paris | |
| Deep Neural Networks for Automatic Lyrics Transcription | |
| Emir Demirel, Queen Mary University of London | |
| 11:45 | Data-Driven Approaches for Query by Vocal Percussion |
| Alejandro Delgado, Queen Mary University of London & Roli | |
| 12:15 | Lunch Break |
| 14:00 | Keynote 1: Applications of Machine Intelligence in Computational Acoustics |
| Augusto Sarti, Politecnico di Milano | |
| 15:00 | Towards Neural Context-Aware Performance-Score Synchronization |
| Ruchit Agrawal, Queen Mary University of London | |
| Autonomous and Robust Live Tracking of Complete Opera Performances | |
| Charles Brazier, Johannes Kepler University Linz | |
| 16:15 | Large-Scale Multi-Modal Music Search and Retrieval without Symbolic Representation |
| Luís Carvalho, Johannes Kepler University Linz | |
| Robust Deep Learning for Music Performance Analysis | |
| Vinod Subramanian, Queen Mary University of London |
Friday 15th October 2021
| 9:00 | Keynote 2: Source Separation Metrics: What are they really capturing? |
| Rachel Bittner, Spotify | |
| 10:00 | Exploration of Music Collections with Audio Embeddings |
| Philip Tovstogan, Universitat Pompeu Fabra | |
| Audio Auto-tagging as Proxy for Contextual Music Recommendation | |
| Karim Ibrahim, Telecom Paris | |
| 11:15 | Deep Learning Methods for Music Style Transfer |
| Ondřej Cífka, Telecom Paris | |
| Exploring Generative Adversarial Networks for Controllable Musical Audio Synthesis | |
| Javier Nistal, Telecom Paris & Sony SCL | |
| 12:15 | Lunch Break |
| 14:00 | Keynote 3: Generative Modeling Meets Deep Learning: A Modern Statistical Approach to Music Signal Analysis |
| Kazuyoshi Yoshii, Kyoto University | |
| 15:00 | Neuro-Steered Music Source Separation |
| Giorgia Cantisani, Telecom Paris | |
| Automatic Characterization and Generation of Music Loops and Instrument Samples for Electronic Music Production | |
| António Ramires, Universitat Pompeu Fabra | |
| 16:00 | Closing |
| Domna Paschalidou, European Research Executive Agency |