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Mary Simoni - Rensselaer Polytechnic Institute. Troy, NY, US

Mary Simoni

Dean, School of Humanities, Arts, and Social Sciences & Professor, Arts | Rensselaer Polytechnic Institute


Works as a leading composer, author, teacher, pianist, and arts administrator

Areas of Expertise (7)

Sound Synthesis

Interactive Electronics

Music Education

Music Theory

Music Composition


Digital Signal Processing


Mary Simoni is a composer, author, teacher, pianist, consultant, arts administrator, and amateur photographer.

Prior to joining Rensselaer in 2011 as Dean of the School of Humanities, Arts and Social Sciences, Simoni served as associate dean of the University of Michigan School of Music, Theatre and Dance. As associate dean, Simoni developed research opportunities and strategic planning processes for the school, developed and directed electronic publications, and launched and directed the University of Michigan record label, Block M Records to promote the vitality, multiplicity, and excellence of the University of Michigan through web-based publication of media-rich scholarly and creative research.

Her work as a pianist specializes in the use of interactive electronics. Her compositions include the design of performance systems that extend the sonic capabilities of traditional acoustic instruments.

“My composition and writing strive to advance the expressive capabilities of the human voice and traditional acoustic instruments,” Simoni said. “My research spans digital signal processing, sound synthesis, algorithmic composition, and the design and programming of new interfaces for musical performance.”

Her music and multimedia works have been performed in Asia, Europe, and throughout the United States and have been recorded by Centaur Records, the Leonardo Music Journal published by the MIT Press, and the International Computer Music Association. She is the recipient of the Prize in Composition by the ArtNET Virtual Museum.

Simoni is the author of the books “A Gentle Introduction to Algorithmic Composition,” and “Analytical Methods of Electroacoustic Music,” and is currently working on a book with Roger Dannenberg of Carnegie Mellon University on algorithmic composition. She has consulted for the Canadian Innovation Foundation, the National Science Foundation, the National Peace Foundation, and numerous universities and arts agencies throughout the world.

She is a Medal Laureate of the Computer World Honors Award for her research in digital music information retrieval, and is a Professor Emerita of Performing Arts Technology at the University of Michigan.

The Knight Foundation, the Kellogg Foundation, the National Science Foundation, and the Michigan Council for the Arts and Cultural Affairs have funded her research.



Mary Simoni Publication Mary Simoni Publication





Education (4)

Michigan State University: Ph.D., Music Theory

Michigan State University: M.Mus., Music Composition

Michigan State University: B.Mus., Music Theory and Composition

Michigan State University: B. Mus., Music Education

Media Appearances (1)

Mary Simoni makes a case for arts in meetings and on stage

Albany Times Union  online


Discerning eyes have been on Mary Simoni ever since she arrived in the Capital Region six months ago. On Friday night, she'll finally be on a real stage.

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Articles (3)

Audification as a Diagnostic Tool for Exploratory Heliospheric Data Analysis

International Conference on Auditory Display

Alexander, Robert L Gilbert, Jason A Landi, Enrico Simoni, Mary Zurbuchen, Thomas H Roberts, D Aaron

2011 To date, scientific data analysis is almost exclusively conducted through the visual modality, though the perceptual benefits of multi-modal stimulation are well known [1]. Visualization tools utilize parameters such as color, size, and shape to render data sets of moderate complexity. However, a growing number of NASA instruments produce extremely large and complex data sets that must be visually rendered in groups of sub-dimensions [2]. One such instrument, the Solar Wind Ion Composition Spectrometer (SWICS) on the Advanced Composition Explorer (ACE) satellite, has measured a large number of solar wind parameters for the last 13 years. The effective navigation and analysis of these massive data sets is a persistent challenge. New data mining tools are necessary in order to fully engage the large number of variables involved with these extremely complex systems. New multi-modal interfaces will have far reaching applications for exploratory heliophysics research. This work will demonstrate that audification is a powerful diagnostic tool for mining and analyzing solar wind data. Through audification, this research has revealed new insight into data parameters used for differentiating solar wind types. For example, an ion charge state ratio previously considered to be unimportant is proving to be a leading indicator of the boundaries between coronal hole and non-coronal hole wind, as discussed below. A deep understanding of space weather, to which the solar wind is a decisive component, will be increasingly important as we continue to explore the space environment.

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Algorithmic Composition: A Gentle Introduction to Music Composition Using Common LISP and Common Music

SPO Scholarly Monograph Series

Mary Simoni

2003 This book is about learning to compose music using the programming language Common LISP and the compositional environment Common Music developed by Heinrich Taube. The motivation for writing this book comes from several years of teaching music and engineering students the fundamentals of algorithmic composition. Algorithmic composition, for the purposes of this book, is defined as the use of computers to implement procedures that result in the generation of music. The idea of applying algorithms during the composition of music is pervasive throughout music history. The intent of this book is to give the reader the fundamentals of Common LISP and Common Music accompanied by examples of algorithmically based compositions. Although not every aspect of Common LISP and Common Music are covered in this book, the reader will be well equipped to develop their own algorithms for composition.

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Time-frequency analysis of musical signals

Proceedings of the IEEE

W.J. Pielemeier ; G.H. Wakefield ; M.H. Simoni

1996 The major time and frequency analysis methods that have been applied to music processing are traced and application areas described. Techniques are examined in the context of Cohen's class, facilitating comparison and the design of new approaches. A trumpet example illustrates most techniques. The impact of different analysis methods on pitch and timbre examination is shown. Analyses spanning Fourier series and transform, pitch synchronous analysis, heterodyne filter, short-time Fourier transform (STFT), phase vocoder, constant-Q and wavelet transforms, the Wigner (1932) distribution, and the modal distribution are all covered. The limitations of windowing methods and their reliance on steady-state assumptions and infinite duration sinusoids to define frequency and amplitude are detailed. The Wigner distribution, in contrast, uses the analytic signal to define instantaneous frequency and power parameters. The modal distribution is shown to be a linear transformation of the Wigner distribution optimized for estimating those parameters for a musical signal model. Application areas consider analysis, resynthesis, transcription, and visualization. The more stringent requirements for time-frequency (TF) distributions in these applications are compared with the weaker requirements found in speech analysis and highlight the need for further theoretical research.

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