Artificial neural networks provide a flexible environment within which we model the mechanics and implied associated cognitive processes involved in human prediction of time ordered sequential musical elements. We model an experientially trained listener's cognition of functional tonal western music. By interpreting the distribution of output activations of the network as expectations for the next event in the sequence and comparing this to the consequential event, we establish a quantifiable measurement of the degree of realized expectation. The strength and distribution of output activations provide a method for modeling:
We propose to design and implement a series of experiments to investigate these implications and to refine and develop new connectionist architectures to build these models. Initial experiments with a compact representation of a limited number of musical dimensions will be followed by a more flexible representation incorporating all the multidimensionality, complexity, and intricacies of a complete musical work.
The lectures from CCRMA's Music 151 course, ``Psychophysics and Cognitive Psychology for Musicians'' are now published as:
This introductory text on psychoacoustics, specifically as it relates to music and computerized sound, emerged from a course that has been taught for many years at Stanford University's Center for Computer Research in Music and Acoustics (CCRMA). Organized as a series of 23 lectures for easy teaching, the book is also suitable for self-study by those interested in psychology and music. The lectures cover both basic concepts, and more advanced concepts illuminated by recent research. Further aids for the student and instructor include sound examples on CD, appendixes of laboratory exercises, sample test questions, and thought problems. The contributors, leading researchers in music psychology and computer music, John Chowning, Perry Cook, Brent Gillespie, Dan Levitin, Max Mathews, John Pierce, and Roger Shepard.
The human auditory system possesses a remarkable ability to differentiate acoustic signals according to the vibrational characteristics of their underlying sound sources. Understanding how listeners can detect, discriminate, classify, and remember acoustic source properties forms this project's long-range goal. The present project brings to bear on these topics techniques of psychophysical measurement, spectral analysis/synthesis techniques, and computer simulation of acoustic objects. Using such interdisciplinary approaches, studies will determine the validity of a three-stage model of auditory source perception:
Using methods of signal detection, preliminary studies will determine how listeners' sensitivity to auditory signals depends on whether attention is first directed to their acoustic features, and how sensitivity may improve as a function of the available source cues. Additional studies will use physical modeling and spectral simplification techniques to determine which acoustic features are critical to detection performance. A fundamental problem in auditory perception is to understand how listeners can perceive a sound source to be constant across wide variations in the range of sounds that the source can produce. Consequently, a separate set of studies will use adaptation techniques to determine how listeners categorize sounds by their source characteristics, and to assess whether computer-generated prototypical sources - sources, such as bars, tubes, and plates, that define broad classes of sound-producing objects - are classified more rapidly and accurately than non-prototypical sources. Our ability to recognize previously heard sounds suggests that we encode features of acoustic sources in memory. A related set of experiments will use recognition and recall tasks to determine what features of sounds are encoded in working and long-term memory, and whether memory representations encode a sound's surface spectral-temporal features or its underlying physical source characteristics.
In sum, this research program should shed important light on the representation of auditory source characteristics by determining the stages of processing that auditory information undergoes from its initial encoding at peripheral levels to its source-based representation at more central levels. Not only can this improve our basic understanding of auditory processing but also can suggest ways in which humans can optimize their performance in detecting and evaluating signals of interest within their acoustic environment.
Broadly speaking, my research is concerned with the psychology of structure and perceptual organization. How does the brain organize the world around us, create categories, and parse a dense perceptual field? To answer these questions, I have been examining principles of visual and auditory perception (how the brain groups basic elements into objects).
More specifically, my current research projects include work on:
For more information, please see http://www-ccrma.stanford.edu/~levitin/research.html.
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