From the Guest Editor---Special Cluster on Computation in Music

The issues in computational modeling of music have risen to the forefront due to the massive, and rapidly growing, amount of music that is now available in digital format. Propelled by the industry’s demand for content-based music indexing, for retrieval and recommendation systems, and for techniques to manipulate and create music for mobile phones, games, and other applications, and fueled by academia’s more established musicological and pedagogical interests, the need for new algorithms and models to better understand, create, and evaluate music has never been greater. This increase in music and computing research is evidenced from the proliferation of new conferences in this area, such as the International Conference on Music Information Retrieval (founded 2000), the Computer Music Modeling and Retrieval Conference (founded 2003), and the Sound and Music Computing Conference (founded 2004), to name a few. The operations research community is not far behind in this respect; I have organized several sessions on music at recent INFORMS conferences. In 2003, the INFORMS Annual Meeting in Atlanta featured a special cluster of three invited sessions devoted to OR in the Arts: Applications in Music; the 9th INFORMS Computing Society conference in Annapolis, MD in 2005 showcased two invited sessions entitled Music, Computation and Artificial Intelligence. One might ask: What has OR got to do with music and computing? The definitions of OR range from the textbook coverage of techniques such as mathematical (including linear, nonlinear, integer, mixedinteger, dynamic) programming and stochastic methods (including Markov modeling and queuing theory) to Richard Larson’s “common-sense approach to problems” with the value added of OR being “the correct framing and formulation of the problem” (OR/MS Today, December 2004). Regardless of the choice of definition, OR is very much part of computation in music today. For example, music-to-score alignment can be modeled and solved using dynamic programming, and the problem of separating voices in polyphonic music can be framed as an optimization problem to be solved using local search techniques. Together with the cluster’s three distinguished associate editors, Roger Dannenberg, Joel Sokol, and Mark Steedman, I am pleased to present to you a selection of five papers on a range of topics in this special cluster on computation in music. The first three articles focus on music description and analysis, followed by papers on music improvisation and evaluation of retrieval systems. The article by Darrell Conklin and Christina Anagnostopoulou proposes a viewpoints method for knowledge representation of segmented melodies that allows for melodic analysis at different levels of abstraction. The proposed method can serve as a tool for stylistic analysis, synthesis, and classification. The authors demonstrate that the viewpoints method facilitates the discovery of recurrent patterns in large collections. The article by Emilia Gomez focuses on tonal description of polyphonic audio. Tonal features can be used in similarity assessment and to index digital collections. The author proposes a method for extracting a tonal descriptor, a vector called a harmonic pitch class profile, from polyphonic audio signals, and applies the proposed description to audio key finding. The article by Elaine Chew describes the spiral array model for tonality and gives an overview of algorithms for key finding, pitch spelling, and tonal segmentation based on this model. The paper introduces the Argus algorithm, a linear-time method, for automatic segmentation by tonal context. These algorithms provide a set of tools for describing the tonal patterns of music encoded in numeric form. In the next article, Judy Franklin summarizes the state of the art in the use of recurrent neural networks for generating music and presents a recurrent neural network that can learn both longand short-term structures in music. The proposed network is applied to jazz improvisation to generate new melodies from the original, given an alternate sequence of chords.