Quantifying musical merit

Abstract

This paper surveys techniques for merit-based decision-making in automated composition and analysis. The paper has two parts, respectively covering the "tactics" and "strategy" of decision-making processes.Part I deals with functions used to assess a candidate's merit (or demerit) quantitatively. The "discrimination" or precision associated with a merit function determines whether it is suitable only for "black and white" judgments of acceptability or if it also can be used for "shades of grey" judgments of preference. Differences between absolute merit (magnitudes) and relative merit (distances) are briefly discussed. The bulk of Part 1 explores ways of reconciling several evaluative factors into a simple numerical measure: logical, additive, geometric, multiplicative, prioritized, and uniform. The introduction of a randomness is advised as a way of distinguishing candidates when their merit assessments would otherwise teeter at the knife edge.Part II discusses how merit functions may be used to guide a decision-making process to an acceptable, preferable, or even optimal solution. Distinction is drawn between "algorithmic" and "heuristic" procedures. Background is provided on heuristic reasoning, as first described by Polya. Newell, Shaw, and Simon's use of heuristics to guide a decision-making process efficiently toward an acceptable solution is contrasted with Ebcioglu's use of heuristics to obtain better-than-acceptable results. A survey of compositional and analytic applications compares relative strengths and weaknesses of different strategies including rote, random, "rule-based" or purely heuristic (e.g. sorting, conditional probability, the "Blackboard" model, Chomsky grammars), generate-and-test, heuristically guided search, and exhaustive search.

Motivations and Methodologies for Automation of the Compositional Process

“…"composing programs such as CHORAL (Ebcioglu, 1992(Ebcioglu, , 1988 and CYBERNETIC COMPOSER (Ames and Domino, 1992) ultimately serve an analytic purpose [...] The difference between active style synthesis [algorithmic composition] [...] and empirical style modelling [computational modelling of musical styles] is simply me extent to which me generative criteria have been devised 'before' or 'after' me fact" (Ames, 1992, p.55).…”

“…Once again, the importance of making such distinctions depends on the fact that different methodological approaches are required in each of the different disciplines. In order to make this argument concrete, we shall consider the examples cited by Ames (1992) in a little more depth.…”

A Cognitive Model and a Knowledge Representation System for Music and Multimedia

“…Recently, a growing number of researchers concentrated their efforts on the development of Al-based systems (Camurri 1990;Camurri and Leman 1994;Ames 1992;Baggi 1992;Dannenberg 1993;Rowe 1993;Wiggins et al 1993). A basic issue on the use of AI for music research concerns "how" to approach AI: our position is toward a bottom-up approach to AI, starting from the psycho-physical and perceptual aspects.…”

“…29-30), is therefore a key issue (a multiparadigm approach, from the point of view of the computational mechanism). For example, taxonomic representations in terms of semantic networks (Brachman and Downloaded by [The University of Manchester Library] at 13:46 20 November 2014 Schmolze 1985) are appropriate for inheritance and classification inference mechanisms; production rules are appropriate for representing logical implications and gave good results in music systems (e.g., Ames 1992); reasoning on actions and plans requires still further mechanisms, such as temporal reasoning; finally, reasoning on the domain by means of "metaphors" is crucial for integrated multimedia representations.…”

Musical blending and creativity: An empirical evaluation of the CHAMELEON melodic harmonisation assistant

“…Creative music systems often fall into two major categories (Pearce & Wiggins, 2001): systems whose goal is to imitate a particular musical genre or the style of a certain composer, and systems that aim to generate novel musical styles; Ames (1992) refers to these two approaches as “empirical style modelling” and “active style synthesis”. Synthesis of novel musical styles is more demanding than the mere modelling of certain musical characteristics of a given style, as its major challenge is to transform or combine existing rules of music making (cf.…”