Computational Models of  Music Cognition

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Computational Models of  Music Cognition by Mind Map: Computational Models of  Music Cognition

1. Computer Models

1.1. Algorithms

1.1.1. Grouping LBDM Proximity rule Similarity - Change rule Weighted Average Derivation

1.1.2. Expectation Static Model Schellenberg - 2 Factor Model Trained Model Huron Testing the Model - Probe Tone

1.1.3. Key Signature K & S Algorithm Profiling of 24 sets of key Profile the test set Correlate the test set profile with 24 set of profile

1.2. Motivation: Why do we want to create computer models?

1.2.1. Algorithmic Composition New Music Structure New Genre New Compositional Technique

1.2.2. Design of Compositional Tools The Pipeline of Music Creation Tools to help musician

1.2.3. Computational Music Analysis To propose and verify Hypothesis Analysis By Synthesis Analysis By Decomposition

1.2.4. Music Cognition Understand How The Mind Work Music Improvisation

2. Mind

2.1. Music Cognition

2.1.1. Perception of Grouping of Structure in Music

2.1.2. Melodic Pitch Expectation

2.1.3. Key Finding

3. Music

3.1. Representation

3.1.1. Symbolic (High level abstraction) Musical Score Midi Files, MusicXML Musical Surface Onset Pitch Duration IOI Rest Contour Scale Degree

3.1.2. Non-Symbolic wav files, MP3, Aiff, ogg (Low Abstraction, precise)

4. Gestalt Principle

4.1. Proximity

4.2. Similarity