Journal ArticleDOI
Artificial Neural Networks that Classify Musical Chords
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TLDR
An artificial neural network was trained to classify musical chords into four categoriesindependent of musical key and it was found that the first layer of connection weights in the network converted the local representations of input notes into distributed representations that could be described in musical terms as circles of major thirds and on circles ofmajor seconds.Abstract:
An artificial neural network was trained to classify musical chords into four categories—major, dominant seventh, minor, or diminished seventh—independent of musical key. After training, the internal structure of the network was analyzed in order to determine the representations that the network was using to classify chords. It was found that the first layer of connection weights in the network converted the local represen tations of input notes into distributed representations that could be described in musical terms as circles of major thirds and on circles of major seconds. Hidden units then were able to use this representation to organize stimuli geometrically into a simple space that was easily partitioned by output units to classify the stimuli. This illustrates one potential contribution of artificial neural networks to cognitive informatics: the discovery of novel forms of representation in systems that can accomplish intelligent tasks.read more
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The computational brain: Patricia S. Churchland and Terrence J. Sejnowski (MIT Press, Cambridge, MA, 1992); xi, 544 pages, $39.95
TL;DR: The Computational Brain this paper provides a broad overview of neuroscience and computational theory, followed by a study of some of the most recent and sophisticated modeling work in the context of relevant neurobiological research.
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A Unified Approach To Fractal Dimensions
TL;DR: A new description of three essential classes of fractal dimensions based on: (1) morphology, (2) entropy, and (3) transforms, all unified through the generalized-entropy-based RA©nyi fractal dimension spectrum is presented.
Journal ArticleDOI
The Expansion of Paths in the Mutual Transformation Mechanism of Music and Narrative
TL;DR: This paper shows the newest prototyping system composed of eight transformation modules including automatic composition process and musical variation process to implement all of circulation routes experimentally with the revision of existing music-based programs.
Book ChapterDOI
Knowledge, Truth, and Values in Computer Science
Timothy R. Colburn,Gary M. Shute +1 more
TL;DR: It is argued (Colburn & Shute, 2007), however, that the nature of computer science abstraction lies in the modeling of interaction patterns, while thenature of mathematical abstraction lies at least as much in the modeled of inference structures.
Proceedings ArticleDOI
Textual affect sensing and affective communication
TL;DR: This research has shown how cognitive variables of the emotion model can be computed from linguistic components in text using a compositionality principle proposed for textual affect interpretation.
References
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Journal ArticleDOI
A Generative Theory of Tonal Music
TL;DR: Aboitiz et al. as discussed by the authors explored the relationships between language, music, and the brain by pursuing four key themes and the crosstalk among them: song and dance as a bridge between music and language; multiple levels of structure from brain to behavior to culture; the semantics of internal and external worlds and the role of emotion; and the evolution and development of language.
Book
A Generative Theory of Tonal Music
Fred Lerdahl,Ray Jackendoff +1 more
TL;DR: Aboitiz et al. as discussed by the authors explored the relationships between language, music, and the brain by pursuing four key themes and the crosstalk among them: song and dance as a bridge between music and language; multiple levels of structure from brain to behavior to culture; the semantics of internal and external worlds and the role of emotion; and the evolution and development of language.
Journal ArticleDOI
The computational brain: Patricia S. Churchland and Terrence J. Sejnowski (MIT Press, Cambridge, MA, 1992); xi, 544 pages, $39.95
TL;DR: The Computational Brain this paper provides a broad overview of neuroscience and computational theory, followed by a study of some of the most recent and sophisticated modeling work in the context of relevant neurobiological research.
Book
The Computational Brain
TL;DR: The Computational Brain addresses the foundational ideas of the emerging field of computational neuroscience, examines a diverse range of neural network models, and considers future directions of the field.
Book
Cognitive Foundations of Musical Pitch
TL;DR: In this paper, a key-finding algorithm based on tonal hierarchical relations was proposed to find the key of a key in a tonal music, and the algorithm was applied to both tonal and non-tonal music.