Cognitive Science Program

MTO at the Leading Edge (2014)
Eric J. Isaacson
Society for Music Theory, 20 (1),

What You See Is What You Get: on Visualizing Music. (2005)
Eric J. Isaacson
6th International Society for Music Information Retrieval Conference,

Abstract

Though music is fundamentally an aural phenomenon, we often communicate about music through visual means. The paper examines a number of visualization techniques developed for music, focusing especially on those devel- oped for music analysis by specialists in the field, but also looking at some less successful approaches. It is hoped that, by presenting them in this way, those in the MIR community will develop a greater awareness of the kinds of musical problems music scholars are concerned with, and might lend a hand toward addressing them

A Music Representation Requirement Specification for Academia (2003)
Donald Byrd, Eric Isaacson
Computer Music Journal, 27 (4), 43-57

Abstract

The Indiana University School of Music is one of the world's largest music schools. Variations2 is a large-scale digital music library project under development at the University. This paper outlines requirements for a symbolic music representations to be used beginning with a future version of Variations2. The primary use of this music representation will be to encode existing scores in Common (Western) Music Notation (CMN).

Music representation in a digital music library (2003)
Donald Byrd, Eric Isaacson
2003 Joint Conference on Digital Libraries,

Abstract

The Variations2 digital music library currently supports music in audio and score-image formats. In a future version, we plan to add music in a symbolic form. We describe our work defining a music representation suitable for the needs of our users.

Content Visualization in a Digital Music Library (2003)
Eric J. Isaacson

Abstract

The Variations2 Digital Music Library project at Indiana University provides the opportunity to develop visualization methods aimed at students in schools of music. This paper describes several schemes involving the visualization of musical content.

Variations2: a digital music library system (2002)
Jon Dunn, Eric J. Isaacson
ACM/IEEE Joint Conference on Digital Libraries,

Abstract

This demonstration will show version 1.0 of the Variations2 digital library system developed by Indiana University. Variations2 is being built to provide access to music in a variety of formats–sound recordings, scanned musical scores, computer score notation files, and video–and is designed to support research and learning in the field of music.

Indiana university digital music library project (2001)
Jon Dunn, Eric J. Isaacson
ACM/IEEE Joint Conference on Digital Libraries,

Abstract

The Indiana University Digital Music Library project plans to create a digital library testbed system containing music in a variety of formats, designed to support research and education in the field of music and to serve as a platform for digital library research. Prototypes of user interfaces to the system will be demonstrated.

Digital music libraries - research and development (2001)
David Bainbridge, Gerry Bernbom, Mary Wallace, Andrew Dillon
ACM/IEEE Joint Conference on Digital Libraries,

Abstract

Digital music libraries provide enhanced access and functionality that facilitates scholarly research and education. This panel will present a report on the progress of several major research and development projects in digital music libraries.

Neural Network Models for the Study of Post-Tonal Music. (1996)
Eric J. Isaacson
Joint International Conference on Cognitive and Systematic Musicology, 237-250

Abstract

Neural networks are used to study two issues pertaining to atonal music. In the first part of the paper, feed-forward neural networks, using a variant of the backpropagation learning algorithm, try to learn a variety of abstract theoretical constructs from pitch-class set theory. First, learning the properties of individual sets is studied. Then a network's ability to learn various relationships between sets is examined. Based on the behavior of the network during learning, conclusions are drawn with regard to perceptual issues relating to pcset theory. In the second part of the paper, an interactive activation and competition (IAC) network is used to parse a musical passage into analytical objects. The paper concludes with suggestions for further research.

Computer Applications in Music Composition and Research (1993)
Gary E. Wittlich, Eric J. Isaacson, Jeffrey E. Hass
Advances in Computers, 36 111-202

Abstract

The chapter describes activities in three areas of computing applications in music: music score input and output, sound generation and manipulation, and research on musical structure and performance. The chapter focuses on ways of representing music scores for computer storage, retrieval, and manipulation, and includes a discussion of two representative music codes, encoding devices and software, and music printing. The chapter discusses computer applications in music composition and includes information about the Musical Instrument Digital Interface(MIDI) standard, digital sound synthesis, and computer-aided composition. The chapter presents a survey of computer applications in music research and includes discussions of database developments, data structures for music analysis, adaptations of artificial intelligence techniques and concepts from cognitive science, and research into expressive musical performance. This chapter describes efforts arising from Algorithmic composition (Al) research whose goal is the composition of music in stylistic imitation of earlier composers. The most promising approaches are those using grammatical approaches and connectionist networks. In conclusion, the chapter presents comments on the current situation in each of these areas and then address briefly three philosophical issues attending the use of computers by musicians.