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Music Informatics Group

Georgia Institute of Technology

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  • EAsT: Embeddings As Teachers for Music Classification
  • Latte: Cross-framework Python Package for Evaluation of Latent-Based Generative Models
  • Score-Informed Networks for Music Performance Assessment
  • Multi-Task Learning for Instrument Activation Aware Music Source Separation
  • AR-VAE: Attribute-based Regularization of VAE Latent Spaces
  • dMelodies: A Music Dataset for Disentanglement Learning
  • Learning to Traverse Latent Spaces for Musical Score Inpainting
  • An Attention Mechanism for Musical Instrument Recognition
  • Explicitly Conditioned Melody Generation
  • Music Informatics Group @ICML Machine Learning for Music Discovery Workshop

Recent Posts

  • EAsT: Embeddings As Teachers for Music Classification
  • Latte: Cross-framework Python Package for Evaluation of Latent-Based Generative Models
  • Score-Informed Networks for Music Performance Assessment
  • Multi-Task Learning for Instrument Activation Aware Music Source Separation
  • AR-VAE: Attribute-based Regularization of VAE Latent Spaces

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    unlabeled data

    The concept of learning from unlabeled data

    Posted on November 6, 2017November 6, 2017Full size 546 × 236

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    Published inAutomatic drum transcription using the student-teacher learning paradigm with unlabeled music data
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