Connectionist representations of tonal music : discovering musical patterns by interpreting artificial neural networks / Michael R.W. Dawson.

Issued in print and electronic formats.

Includes bibliographies and index.

Cover; Half Title; Title; Copyright; Contents; List of Figures; List of Tables; Acknowledgements; Overture: Alien Music; Chapter 1: Science, Music, and Cognitivism; 1.1 Mechanical Philosophy, Mathematics, and Music; 1.2 Mechanical Philosophy and Tuning; 1.3 Psychophysics of Music; 1.4 From Rationalism to Classical Cognitive Science; 1.5 Musical Cognitivism; 1.6 Summary; Chapter 2: Artificial Neural Networks and Music; 2.1 Some Connectionist Basics; 2.2 Romanticism and Connectionism; 2.3 Against Connectionist Romanticism; 2.4 The Value Unit Architecture; 2.5 Summary and Implications.

Chapter 3: The Scale Tonic Perceptron3.1 Pitch-Class Representations of Scales; 3.2 Identifying the Tonics of Musical Scales; 3.3 Interpreting the Scale Tonic Perceptron; 3.4 Summary and Implications; Chapter 4: The Scale Mode Network; 4.1 The Multilayer Perceptron; 4.2 Identifying Scale Mode; 4.3 Interpreting the Scale Mode Network; 4.4 Tritone Imbalance and Key Mode; 4.5 Further Network Analysis; 4.6 Summary and Implications; Chapter 5: Networks for Key-Finding; 5.1 Key-Finding; 5.2 Key-Finding with Multilayered Perceptrons; 5.3 Interpreting the Network; 5.4 Coarse Codes for Key-Finding.

5.5 Key-Finding with Perceptrons5.6 Network Interpretation; 5.7 Summary and Implications; Chapter 6: Classifying Chords with Strange Circles; 6.1 Four Types of Triads; 6.2 Triad Classification Networks; 6.3 Interval Cycles and Strange Circles; 6.4 Added Note Tetrachords; 6.5 Classifying Tetrachords; 6.6 Interpreting the Tetrachord Network; 6.7 Summary and Implications; Chapter 7: Classifying Extended Tetrachords; 7.1 Extended Tetrachords; 7.2 Classifying Extended Tetrachords; 7.3 Interpreting the Extended Tetrachord Network; 7.4 Bands and Coarse Coding; 7.5 Summary and Implications.

Chapter 8: Jazz Progression Networks8.1 The ii-V-I Progression; 8.2 The Importance of Encodings; 8.3 Four Encodings of the ii-V-I Problem; 8.4 Complexity, Encoding, and Training Time; 8.5 Interpreting a Pitch-class Perceptron; 8.6 The Coltrane Changes; 8.7 Learning the Coltrane Changes; 8.8 Interpreting a Coltrane Perceptron; 8.9 Strange Circles and Coltrane Changes; 8.10 Summary and Implications; Chapter 9: Connectionist Reflections; 9.1 A Less Romantic Connectionism; 9.2 Synthetic Psychology of Music; 9.3 Musical Implications; 9.4 Implications for Musical Cognition; 9.5 Future Directions.

Intended to introduce readers to the use of artificial neural networks in the study of music, this volume contains numerous case studies and research findings that address problems related to identifying scales, keys, classifying musical chords, and learning jazz chord progressions. A detailed analysis of networks is provided for each case study which together demonstrate that focusing on the internal structure of trained networks could yield important contributions to the field of music cognition.

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