Melodic Cue Abstraction, Similarity, and Category Formation: A Formal Model

Abstract

In the first part of this article, the notions of identity, similarity, categorization, and feature salience are explored; musical examples are provided at various stages of the discussion. Then, formal working definitions are proposed that inextricably bind these concepts together. These definitions readily lend themselves to the development of a formal model for clustering-the Unscramble algorithm-which, given a set of objects and an initial set of properties, generates a range of plausible categorizations for a given context. Finally, as a test case, the clustering algorithm is used to organize a number of melodic segments, taken from a monophonic piece by J. S. Bach, into motivic categories; the algorithm also determines a prototype for each cluster and uses these prototypical descriptions for membership prediction tasks. The results of the computational system are compared with the empirical results obtained for the same data in two earlier studies (I. Deliège, 1996Deliège, , 1997.O NE significant component of musical understanding is the ability of listeners to cluster musical materials together into categories such as motives, themes, and so on. Salient musical cues enable listeners to make similarity judgments between various musical materials and to organize these into meaningful groups. In this study, the notions of feature salience,

MIDI to SP-MIDI Transcoding Using Phrase Stealing

“…The phrase stealing algorithm keeps events that a listener expects to hear [11]. It usually preserves the melody and phrase lines, that is, those elements that listeners are the most likely to be listening to closely and following.…”

Modeling Melody Similarity Using Music Synthesis and Perception

“…Margaret Cahill [6] focused on listening experiments to gather similarity ratings for a piece in theme and variations part. Emilios Cambouropoulos [7] has focused on fundamental concepts of identity, similarity, categorization and melodic cue and proposed an algorithm for monophonic tunes. Ning Hu [8] presented a work on sung queries and retrieval for query by humming (QBH).…”

The effect of implied harmony, contour and musical expertise on judgments of similarity of familiar melodies

“…Cambouropoulos (2000) notes that musical context can affect judgments of the similarity of auditory patterns. Levitin (1999) proposed that transformations in timbre, pitch, loudness, spatial location, starting pitch (or transposition - Van Egmond & Povel, 1996), reverberation and tempo across a melodic transformation, in general, do not affect the identity of a melody.…”

“…Davidson, 1985;Edworthy, 1985;Carterette et al 1986;Jones et al 1987;He´bert & Peretz, 1997;Halpern et al 1998;Cambouropoulos & Widmer, 2000;Byrd & Crawford, 2002). In contrast, implied harmony has received relatively little explicit attention in studies of melodic similarity (important exceptions are Sloboda & Parker, 1985;Stoll & Parncutt, 1987;Trainor & Trehub, 1994b;Holleran et al 1995).…”