Music and schema theory : cognitive foundations of systematic musicology
著者
書誌事項
Music and schema theory : cognitive foundations of systematic musicology
（Springer series in information sciences, 31）
Springer, 1995
- : [softcover]
大学図書館所蔵 件 / 全1件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
"Softcover reprint of the hardcover 1st edition 1995."--T.p. verso
Includes bibliographical references (p. [211]-224) and index
内容説明・目次
内容説明
Music is an important domain of application for schema theory. The perceptual structures for pitch and timbre have been mapped via schemata, with results that have contributed to a better understanding of music perception. Yet we still need to know how a schema comes into existence, or how it functions in a particular perception task. This book provides a foundation for the understanding of the emergence and functionality of schemata by means of computer-based simulations of tone center perception. It is about how memory structures self-organize and how they use contextual information to guide perception.
目次
1. Introduction.- 2. Tone Semantics.- 2.1 The Problem of Tone Semantics.- 2.2 Historical Background.- 2.3 Consonance Theory.- 2.4 Cognitive Structuralism.- 2.5 The Static vs. Dynamic Approach.- 2.6 Conclusion.- 3. Pitch as an Emerging Percept.- 3.1 The Two-Component Theory of Revesz.- 3.2 Attribute Theory Reconsidered.- 3.3 The Shepard-Tone.- 3.4 Paradoxes of Pitch Perception.- 3.5 The Shepard-Illusion.- 3.6 Ambiguous Stimuli.- 3.7 Conclusion.- 4. Defining the Framework.- 4.1 The Computer Model.- 4.2 Representational Categories.- 4.2.1 Signals.- 4.2.2 Images.- 4.2.3 Schemata.- 4.2.4 Mental Representations.- 4.3 Conclusion.- 5. Auditory Models of Pitch Perception.- 5.1 The Missing Fundamental.- 5.2 Auditory Models.- 5.3 SAM: A Simple Model.- 5.3.1 SAM - The Acoustical Representation.- 5.3.2 SAM - The Synthetic Part.- 5.4 TAM: A Place Model.- 5.4.1 TAM - The Analytic Part.- 5.4.2 TAM - The Synthetic Part.- 5.4.3 TAM - Examples.- 5.5 VAM: A Place-Time Model 53.- 5.5.1 VAM - The Analytic Part.- 5.5.2 VAM - The Synthetic Part.- 5.5.3 VAM - Examples.- 5.6 Conclusion.- 6. Schema and Learning.- 6.1 Gestalt Perception.- 6.2 Tone Semantics and Self-Organization.- 6.2.1 Self-Organization as Learning.- 6.2.2 Self-Organization as Association.- 6.3 SOM: The Self-Organizing Map.- 6.3.1 Reduction of Dimensionality.- 6.3.2 Analogical and Topological Representations.- 6.3.3 Statistical Modeling.- 6.4 Architecture.- 6.5 Dynamics.- 6.6 Implementation.- 6.7 Conclusion.- 7. Learning Images-out-of-Time.- 7.1 SAMSOM.- 7.1.1 Selection of Data.- 7.1.2 Preprocessing.- 7.1.3 Network Specifications.- 7.1.4 Aspects of Learning.- 7.1.5 Ordering and Emergence.- 7.1.6 Conclusion.- 7.2 TAMSOM.- 7.2.1 Selection of Data and Preprocessing.- 7.2.2 Network Specifications.- 7.2.3 Ordering and Emergence.- 7.3 VAMSOM.- 7.3.1 Selection of Data and Preprocessing.- 7.3.2 Network Specifications.- 7.3.3 Ordering and Emergence.- 7.3.4 Tone Center Relationships.- 7.4 Conclusion.- 8. Learning Images-in-Time.- 8.1 Temporal Constraints in Tonality Perception.- 8.2 Tone Images-in-Time.- 8.3 Tone Context Images.- 8.4 Determination of the Integration Period.- 8.5 TAMSOM.- 8.5.1 Selection of Data and Preprocessing.- 8.5.2 Network Specifications.- 8.5.3 Aspects of Learning.- 8.5.4 Aspects of Ordering and Emergence.- 8.6 VAMSOM.- 8.6.1 Selection of Data and Preprocessing.- 8.6.2 Network Specifications and Aspects of Learning.- 8.6.3 Aspects of Ordering and Emergence.- 8.7 Conclusion.- 9. Schema and Control.- 9.1 Schema-Based Dynamics.- 9.2 TCAD: Tone Center Attraction Dynamics.- 9.2.1 Schema Responses as Semantic Images.- 9.2.2 Images as States.- 9.3 TCAD - Stable States.- 9.4 TCAD - Recognition.- 9.5 TCAD - Interpretation.- 9.6 The TCAD Model.- 9.6.1 Definitions.- 9.6.2 Dynamics.- 9.7 TCAD - At Work.- 9.8 Conclusion.- 10. Evaluation of the Tone Center Recognition Model.- 10.1 Overview of Other Models.- 10.2 TCAD-Based Tone Center Analysis.- 10.3 The Evaluation Method.- 10.4 Bartok - Through the Keys.- 10.4.1 Analysis.- 10.4.2 Discussion.- 10.5 Brahms - Sextet No. 2.- 10.5.1 Analysis.- 10.5.2 Discussion.- 10.6 Chopin - Prelude No. 20.- 10.6.1 Analysis.- 10.6.2 Discussion.- 10.7 The Effect of Phrase - Re-evaluation of Through the Keys.- 10.8 Conclusion.- 11. Rhythm and Timbre Imagery.- 11.1 Models of Rhythm Perception.- 11.2 VRAM: A Rhythm Analysis Model.- 11.2.1 Detection of Periodicities.- 11.2.2 VRAM - Analysis.- 11.2.3 VRAM - Examples.- 11.2.4 VRAM - Discussion.- 11.3 The Analysis of Timbre.- 11.4 Conclusion.- 12. Epistemological Foundations.- 12.1 Epistemological Relevance.- 12.2 Neurophysiological Foundations.- 12.2.1 Foundations of Images.- 12.2.2 Foundations of Schemata.- 12.3 Modular Organization.- 12.4 Relevance for a Theory of Meaning.- 12.4.1 Expressive Meaning and Analogical Thinking.- 12.4.2 Expressive Meaning and Virtual Self-movement.- 12.5 Music Semantics and Meaning Formation.- 12.6 Epistemological Principles.- 12.6.1 Atomism vs. Continuity.- 12.6.2 Cartesian Dualism vs. Monism.- 12.6.3 Computational Formalism vs. Complex System Dynamics.- 12.6.4 Representational Realism vs. Naturalism.- 12.6.5 Methodological Solipsism vs. Methodological Ecologism.- 12.6.6 Cognitivism vs. Materialism.- 12.7 Conclusion.- 13. Cognitive Foundations of Systematic Musicology.- 13.1 Cognitive Musicology, AI and Music, and Systematic Musicology.- 13.2 Historical-Scientific Background.- 13.3 New Developments in the 1960s.- 13.4 A Discipline of Musical Imagery.- 13.5 A Psycho-morphological Account of Musical Imagery.- 13.6 Interdisciplinary Foundations.- 13.7 General Conclusion.- A. Orchestra Score in CSOUND.- A.l The Orchestra File.- A. 2 The Score File.- B. Physiological Foundations of the Auditory Periphery.- B.1 The Ear.- B.1.1 The Outer Ear.- B.1.2 The Middle Ear.- B.1.3 The Inner Ear.- B.2 The Neuron.- B.2.1 Architecture.- B.2.2 Analysis of Neuronal Activity.- B.3 Coding.- B.3.1 Spatial Coding.- B.3.2 Temporal Coding.- B.3.3 Intensity.- B. 4 The Brain Stem and Cortex.- C. Normalization and Similarity Measures.- C. l Similarity Measures.- C.2 Towards a Psychoacoustic-Based Similarity Measure.- References.
「Nielsen BookData」 より
