Final Workshop

Online, October 14-15, 2021

The final workshop includes presentations by the fellows of the main results of their research over the last three years as well as keynotes by several distinguished researchers in the field (announcement).

Keynotes

  • Applications of Machine Intelligence in Computational Acoustics
    Augusto Sarti, Politecnico di Milano
  • Source Separation Metrics: What are they really capturing?
    Rachel Bittner, Spotify
  • Generative Modeling Meets Deep Learning: A Modern Statistical Approach to Music Signal Analysis
    Kazuyoshi Yoshii, Kyoto University

Fellows’ Presentations

October 14

  • 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
  • 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
  • Data-Driven Approaches for Query by Vocal Percussion
    Alejandro Delgado, Queen Mary University of London & Roli
  • 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
  • Large-Scale Multi-Modal Music Search and Retrieval without Symbolic Representation
    Luís Carvalho, Johannes Kepler University Linz
  • Investigating the Behaviour of Audio Classification Models through Adversarial Attacks and Gradient based Interpretability Methods
    Vinod Subramanian, Queen Mary University of London

October 15

  • Exploration of Music Collections with Audio Embeddings
    Philip Tovstogan, Universitat Pompeu Fabra
    PDF
  • Audio Auto-tagging as Proxy for Contextual Music Recommendation
    Karim Ibrahim, Telecom Paris
  • 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 CSL
  • Neuro-Steered Music Source Separation
    Giorgia Cantisani, Telecom Paris
    YouTube PDF
  • Automatic Characterization and Generation of Music Loops and Instrument Samples for Electronic Music Production
    António Ramires, Universitat Pompeu Fabra