Music Perception © 199.9 by the regents of the Winter 1999, Vol. 17, No. 2, 261-274 university of California

Book Reviews

Walter B. Hewlett & Eleanor Selfridge-Field (Eds.), Melodie Similarity: Concepts, Procedures, and Applications (Computing in Musicology 11). Stanford, CA: Center for Computer Assisted Research in the Humani- ties (CCARH), and Cambridge, MA: MIT Press, 1998. ix + 244 pp., ISBN 0-262-58175-2, $28 (paper).

Computing tools are now essential in a wide range of disciplines. Melodic Similarity: Concepts, Procedures, and Applications, edited by Walter Hewlett and Eleanor Selfridge-Field, provides an overview of critical issues related to computing in musicology, focusing on investigations of melodic similar- ity. The book is a special issue of the journal Computing and Musicology and is co-published by the Center for Computer Assisted Research in the Humanities (CCARH) and The MIT Press. It is dedicated to the late Helmut Schaffrath, who was central to the development of the Essen database of songs and of many tools for locating melodic patterns within such data- bases.

The publication of a book on melodic similarity represents a landmark in musicology and music cognition. Given the significance of this topic, the project seems long overdue. Melodic similarity plays a critical role in music, providing coherence and unity in a piece, while allowing innova- tion and variety. Composers often find a balance between similarity and contrast. Too much similarity at the foreground, though providing unity, prevents the emergence of hierarchic structure. By presenting somewhat different ideas at the foreground, composers encourage listeners to unite musical ideas at higher hierarchic levels. Too much contrast at the fore- ground, conversely, makes it difficult for listeners to abstract higher level structure.

The book is organized into four sections. Section 1, Concepts and Pro- cedures, consists of six chapters dealing with the concept of melodic simi- larity and techniques of melodic pattern matching. Section 2, Tools and Applications, includes a description of two applications for melodic analy- sis, and a chapter on Nagauta Shamisen music. Section 3, Human Melodic Judgments, includes a chapter on music copyright infringement suits and a chapter on differences between real and artificial folk songs. In the fourth section, Online Tools for Melodic Searching, two valuable Web-based me- lodic search tools are described.

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262 Book Reviews

This organization provides coherence to a highly varied set of chapters. The first chapter makes up more than a quarter of the book; the remaining 12 chapters are relatively short reports. In some cases, brevity seems ap- propriate, but many of these chapters are like extended abstracts, lacking the detail needed to assess their scholarly merit. In addition, aside from Chapter 1, there are virtually no references to the extensive body of psy- chological work on melody, even though melodic similarity is ultimately a psychological phenomenon (see Schmuckler, 1999). On the whole, how- ever, one is still left with an appreciation of both the power of computer analyses (e.g., rapid examination of a very large corpus of music, exhaus- tive searches) and its current limitations (e.g., difficulties in representing melody, false matches).

In Chapter 1, Eleanor Selfridge-Field provides a comprehensive over- view of issues related to melodic similarity and pattern-search algorithms. Special attention is given to the challenges associated with the development of tools that allow users to search music databases for melodic patterns, motifs, and themes. Existing databases of music, such as the Essen collec- tion and Musedata, are very large, allowing researchers to make strong generalizations. However, it can be difficult to extract "the melody" be- cause of the various ways in which melodies are disguised.

For example, in self-accompanying melodies, some pitches pertain both to the thematic idea and to the harmonic or rhythmic support. In roving melodies, the theme migrates from part to part (e.g., soprano to alto). In distributed melodies, the defining notes are divided between parts. Rests, repeated notes, and grace notes further complicate the task of pattern match- ing.

Another challenge is that themes do not always coincide with incipits (the beginning phrase of the music). Computer-generated catalogues of themes tend to be based on incipits. This approach is reasonable for early music and folk-song repertories, but over the 18th and 19th centuries the value of melodic information in incipits gradually decreases. So far, no one has developed a program that can automatically select the "theme" from within a stream of melodic information. At present, themes that are not coincident with incipits are determined by ear and hand-fed to the computer.

A number of common approaches to melodic comparison are discussed, such as those based on contour, chroma, and intervallic pattern. The de- gree of detail identified in a search depends on the goal of the investigation. Precise pitches are often useful for analytical tasks in music history and theory, whereas more abstract representations may be preferred in psycho- logical studies.

In Chapter 2, Donncha Ó Maidin describes a simple example of a me- lodic comparison algorithm. The algorithm, called DIFFERENCE, provides a numerical measure of the melodic difference between two segments of

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Book Reviews 263

duration r. The notes compared are those that occur at the same point in time (when the segments are equated for tempo). The difference score is based on a sum of pitch differences that occur at each moment in time, where each pitch difference is weighted according to the duration and met- ric stress of the pitches involved. Ó Maidin indicates that the algorithm is based loosely on principles of music perception, but it is not clear if the algorithm is predictive of human judgments. Clearly, it would be useful to assess the psychological validity of such an algorithm.

Next, Tim Crawford, Costas Iliopoulos, and Rajeev Raman survey algo- rithmic methods for solving typical problems in music analysis. After de- fining a number of terms, they consider various problems. As an example: given a sequence of notes, identify repeated patterns in that sequence. Vari- ous methods are cited for each type of problem, though the authors provide no discussion of the psychological or musical significance of the examples.

Chapter 4, by Lloyd Smith, Rodger McNab, and Ian Witten, provides a valuable discussion of general algorithms for sequence-based melodic com- parison. The authors describe how one can search for melodic strings in a database of songs using an approach called dynamic programming. Devel- oped for matching DNA sequences, the approach has more recently been adapted for musical applications. It is based on the concept of "edit dis- tance" - the cost of changing one sequence (the source string) into a sec- ond sequence (target string). The cost is calculated according to operators such as replace, insert, and delete. Each operation incurs a cost, or weight. For example, if the cost of replacing a note were weighted by pitch dis- tance, then changing E to F would incur a cost of 1. In melodic compari- son, one might weight pitch changes according to distance in semitones, difference in tonal hierarchy value, or some other metric.

Next, the authors report a study of the number of notes required to match a melodic fragment under various conditions (i.e., the type of infor- mation entered) and of the number of matches returned when fewer notes are entered. If exact interval and rhythm are specified, then about five notes are required to return just one match (otherwise several false matches are returned). If only contour is specified, however, close to 10 notes are re- quired to return just one match. In general, the number of matches re- turned is logarithmically related to the number of notes entered. The ap- proach holds great promise for research and is used both in the HUMDRUM analysis suite ("simil") and in the extraordinary Web page described in chapter 12 by David Bainbridge.

John Howard next discusses the relative effectiveness of different kinds of thematic searching, focusing on the issue of attribution in music cata- loguing. He considers an examination of 144 works of uncertain origin. Various features of incipits (the initial notes in a song) are used as a basis for attribution. One observation is that features commonly thought to be

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264 Book Reviews

insignificant, such as clefs, bar-line placement, and rests, can be quite infor- mative.

In the final chapter of this section, David Cope outlines a useful distinc- tion between two properties of music that may be associated with compo- sitional style: signatures and earmarks. Signatures are defined as motifs that are common to two or more works of a given composer and usually occur between 4 and 10 times in any given work. They are usually two to five beats in length and may be composites of melodic, harmonic, and rhyth- mic elements. According to Cope, signatures define what makes each com- poser unique. Whereas signatures are specific motifs, earmarks are more generalized patterns or passages (e.g., an arpeggio followed by a trill). The significance of earmarks is in where they are used in a composition. There is a brief discussion of how pattern-matching techniques can be used to identify such patterns, and Cope provides an example of a computer-as- sisted composition based on these ideas. All in all, the chapter provides a useful introduction to Cope's important work.

The second section of the book, "Tools and Applications," focuses on existing applications for melodic analysis and production. Dominik Hörnel describes a neural network system that classifies motifs and produces me- lodic variations in the style of Bach and Pachelbel. Such an approach ac- knowledges that although human identification of musical style is quite accurate, it does not rely on the abstraction of simple defining features. Recognition of signature motifs may contribute to style identification in some cases (as Cope suggests), but exemplars of a musical style may often have only a family resemblance to one other. Neural networks are well suited to this type of learning.

Trained with pieces by Bach and Pachelbel, the system was able to learn and reproduce higher order elements of harmonic, motivic, and phrase struc- ture. An important aspect of the network is its multiscale structure: one system harmonizes the melody (Harmonet), another classifies motifs (the supernet), and a third generates the notes that make up the melodic varia- tions (the subnet). The variations produced are somewhat limited, being restricted to a series of 16th notes (which replace a series of quarter notes). Nonetheless, even this problem is not trivial, and examples of variations produced by the network give readers a sense for the output generated.

In Chapter 8, Nigel Nettheim describes a program called MuSearch, which can be used to address questions of pitch, rhythm, meter, and text in music. An advantage of his program is that its input and output may be in the form of conventional notation. A disadvantage, however, is that each task requires a user to write the appropriate code in "C" programming lan- guage and then recompile the program. The program processes music en- coded in the music-printing package SCORE. As there is no large database of music in this format, Nettheim has written another program (EsSCORE) that converts the Essen database files to SCORE files.

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Masato Yako next discusses melodic types and rhythm in Nagauta Shamisen music. The Shamisen is a three-stringed lute used in Japan since the 17th century, often as an accompaniment to singing. Nagauta are love songs traditionally associated with Kabuki, but in the past century they have been valued as an independent art form. With a form of computer- assistance that is not described, Yako has initiated the important project of cataloguing melodic types in Nagauta Shamisen music. Yako also discusses the complexities of understanding rhythm in this music.

The next two chapters make up the section entitled "Human Melodic Judgments." Both chapters are engaging and informative, although neither are representative of the enormous body of psychological research related to melodic similarity. Charles Cronin provides a fascinating discussion of how U.S. courts have examined melodic similarity in works that are the subject of infringement claims. In most cases, such claims are based on melodic similarities, while rhythm and harmony are strangely ignored. Melody, it is believed, is the "fingerprint" of music, and the primary source of economic value. Although this belief is probably incorrect and certainly simplistic, it means that issues of melodic similarity take center stage in such claims. An interesting point is that a charge of infringement can usu- ally be defeated if it can be demonstrated that the melodic fragment in question is also similar to a melodic fragment in some public-domain song. In view of the latter point, a defendant might be wise to conduct a pattern- matching search of thousands of public-domain folk songs. Cronin dis- cusses several well-known cases, with reference to a score of the melodies involved.

Ewa Dahlig and the late Helmut Schaffrath describe a fascinating study in which listeners were presented natural and artificial folk songs and judged which songs were original and which were artificial. It is remarkable that such a Turing test is so rarely conducted, and from that perspective the study represents somewhat of a landmark. Interestingly, authentic melo- dies were not necessarily the ones judged as being "original." Put differ- ently, the artificial melodies passed the Turing test. The qualities associated with "original folk songs" were rhythmic similarity of phrases, a final ca- dence on the first degree of the scale, and an intermediate phrase that did not begin on the first degree. There was also an association between prefer- ence and judgments that the music was "original."

In the concluding section of the book, two exciting Web-based search tools are described. David Bainbridge first describes a melodic locator ser- vice called MELDEX, developed at the University of Waikato, New Zealand. In the music section of the Web site (www.nzdl.org), the user can simply sing a tune into the computer microphone and then conduct a search for that theme among a database of close to 10,000 folk songs (the Digital Tradition and Essen databases). Exact and approximate matches are re- turned, ranked by their similarity to the tune. Similarity is determined by

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266 Book Reviews

one of two methods, including an algorithm based on dynamic program- ming (discussed in Chapter 4).

Andreas Kornstädt next describes an equally impressive tool called THEMEFINDER, accessible through the CCARH MuseData Website (musedata.stanford.edu/shadow/databases/themefinder). In this case, only exact matches are returned, but the user can specify abstract properties of the melody, such as contour, pitch class, and interval size. The repertory can also be restricted by composer, genre, key, and meter. The search soft- ware, developed by David Huron, uses musical data in the KERN format.

With this publication, Computing in Musicology promises to be among the essential journals for musicologists and music psychologists. The issues covered in this volume are relevant to a wide range of research concerns and underscore the enormous potential for computer tools to answer many of the abiding questions in musicology and music cognition. Although some of the chapters are somewhat slight - the authors seemingly reluctant to part with details - the book provides an excellent overview of melodic simi- larity and related issues.1

William Forde Thompson York University

References Schmuckler, M. A. (1999). Testing models of melodic contour similarity. Music Perception,

26,295-326.

Niall Griffith & Peter M. Todd (Eds.), Musical Networks: Parallel Dis- tributed Perception and Performance. Cambridge, MA: MIT Press, 1999. xv + 385 pp., ISBN 0-262-07181-9, $37.50 (cloth).

With the development of computers and the progress in computational methods, computer modeling has started to provide new insights into vari- ous phenomena related to music. Offering a method for quantitative test- ing of theories, it can help us to learn more about various cognitive and perceptual processes related to music comprehension and production, as well as questions concerning musical style.

In many music-related problems, the traditional rule-based artificial in- telligence approach is difficult to apply or even inadequate. This can be the case, for example, when the rules that define the problem cannot be found easily or when the process of interest involves learning. Parallel distributed

1. Address correspondence to William Forde Thompson, Department of Psychology, Atkinson College, York University, 4700 Keele St., Toronto, Ontario, Canada, M3J 1P3.

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