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LEARNING TO ALIGN POLYPHONIC MUSIC
Shai Shalev-Shwartz
, Joseph Keshet
, Yoram Singer
Hebrew University of Jerusalem
Research output
:
Contribution to conference
›
Paper
›
peer-review
15
Scopus citations
Overview
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Dive into the research topics of 'LEARNING TO ALIGN POLYPHONIC MUSIC'. Together they form a unique fingerprint.
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Keyphrases
Symbolic Representation
100%
Polyphonic music
100%
Acoustics
50%
Support Vector Machine
50%
Training Set
50%
Iterative Algorithm
50%
Formal Properties
50%
Target Alignment
50%
Vector Space
50%
HMM-based
50%
Supervised Learning Method
50%
Hidden Markov Model
50%
Musical Piece
50%
Learning Support
50%
Discriminative Methods
50%
Generative Methods
50%
Discriminative Approach
50%
Piano music
50%
Acoustic Representation
50%
Efficient Learning Algorithm
50%
Computer Science
Support Vector Machine
100%
Iterative Algorithm
100%
Supervised Learning
100%
Learning Algorithm
100%
Learning Approach
100%
Discriminative Method
100%
Earth and Planetary Sciences
Acoustics
100%
Vector Space
50%
Hidden Markov Model
50%
Supervised Learning
50%
Support Vector Machine
50%
Physics
Acoustics
100%
Supervised Learning
50%