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Deep Learning for Music Unmixing

Problem Statement

Funny slide

Classical Approaches: Unsupervised

  • NMF
  • KAM
  • short intro into source-filter models, NMF, repet, kam...
  • limitations

Paradigna shift

  • when?
  • why? data, separation is only well defined for certain kind of data

Supervised Source Separation

Supervised Source Separation

  • Classification vs Regression Binary Masking, Deep Clustering

  • Discriminate vs Generative

  • Single I/O vs Multiple I/O Siamese networks, Deep-U net

  • Time Series vs. Frequency

  • its not clear if its a discriminate or generative problem

  • permutation problem

Baseline System (Popular Decisions)

  • BiLSTM - Sequence to Sequence
  • PreProcessing
  • Structure
  • Training
  • Testing
  • Explain UHL2 (BiLSTM) == state of the art
  • deep clustering

SiSEC2018 Demotime

How hip is Source separation

  • End-to-End Networks
  • Fully Convolutional Networks
  • Batch Normalization
  • GANs
  • Capsule Networks
  • Attention Networks
  • Reinforcement Learning

Research Directions

  • Unsupervised Learning to solve the permutation problem
  • Learning with limited amount of data
  • Improving Representations vs End-to-End (WaveNet)
  • datasets accessible for non-audio experts, require significant domain knowledge
  • Convergence of Signal Processing and DL
  • Model long term dependency