MODELING MELODIC DICTATION
David John Baker
Chapter 1 Significance of the Study
All students pursuing a Bachelor’s degree in Music from universities accredited by the National Association of Schools of Music must learn to take melodic dictation (NASM 2019, sec. VIII.6.B.2.A). Melodic dictation is a cognitively demanding process that requires students to hear a melody, then without any access to an external reference, transcribe the melody within a limited time frame. As of 2019, there are 643 Schools of Music belonging to National Association of Schools of Music (NASM)1, meaning that thousands of students every year will be expected to learn this challenging task as part of their aural skills education. The implicit logic is that as one improves in their ability to take melodic dictation, this practice of critical and active listening develops one’s ability to “think in music” (Best 1992; Karpinski 2000) and thus become a more competent musician.
Despite its ubiquity in curricula within School of Music settings, research that explains how people learn to take melodic dictation is limited at best. The fields of music theory and cognitive psychology are best positioned to make progress on this question, but often the skills required to be well versed in these subjects do not overlap in formal training, findings related to this question are published in separate journals, and thus the overlapping research is scarce. This problem is not new and there have been repeated attempts to bridge the gap between practitioners of aural skills and researchers in cognitive psychology (Butler and Lochstampfor 1993; Butler 1997; Klonoski 2006, 2000; Pembrook and Riggins 1990; Karpinski 2000). Literature from music theory has established conceptual frameworks regarding aural skills (Karpinski 2000), cognitive psychology literature has explored factors that might contribute to melodic perception (Dowling 1990, 1978, 1991; Halpern and Bartlett 2010), and applied literature from the field of music education (Buonviri 2017; Nathan O Buonviri and Paney 2015; Paney 2016) has investigated how people learn melodies in more ecological settings.
However, despite these isolated areas of research, we as music researchers do not have a clear understanding of exactly what contributes to the process of how individuals learn melodies (Halpern and Bartlett 2010). This is peculiar since “how does one learn a melody?” seems to be one of the fundamental questions to the fields of music theory, music psychology, as well as music education. This chasm in the literature also raises a disconcerting question in music pedagogy:
If we as pedagogues do not have a in-depth understanding of how people learn melodies, how can we fairly assess what students can be expected to accomplish in the classroom and then fairly grade them on their attempts?
This dissertation aims to fill the gap in the literature between aural skills practitioners and music psychologists in order to reach conclusions that can be applied systematically in pedagogical contexts. I accomplish this by synthesizing literature from music theory, music psychology, and music education in order to demonstrate how tools from both cognitive psychology as well as computational musicology can be used to help inform pedagogical practices.
1.2 Chapter Overview
In the first chapter, I begin by extending the work of Gary Karpinski (Karpinski 2000, 1990) in order to introduce the process of melodic dictation and discuss factors that might play a role in a student’s ability to take melodic dictation. The chapter introduces both a theoretical background and rationale for using methods from both computational musicology and cognitive psychology in order to answer questions about how individuals learn melodies. In order to organize the disparate literatures, I put forward a taxonomy of factors that are assumed to contribute to an individual’s ability to take melodic dictation and discuss each in turn. This chapter outlines the factors hypothesized to contribute to an individual’s ability to learn melodies, incorporating both individual and musical parameters. I conclude the chapter with a discussion of some philosophical and theoretical problems when attempting to measure issues concerning melodic dictation and argue for the advantages of answering this problem using a polymorphic view of musicianship (Levitin 2012; Peretz 2006; Baker et al. 2018).
The second chapter of the dissertation investigates individual factors that are theorized to contribute to melodic dictation. I argue that because the first two steps of Karpinski’s model of melodic dictation do not require any musical training, teasing apart the individual factors that contribute to melodic dictation can be done using a memory for melodies paradigm. I interpret the results of an experiment to highlight the importance of working memory processes in melodic dictation. The chapter corroborates claims by Berz (1995) on the importance of understanding individual differences in working memory capacity and establishes rationale for including differences in cognitive ability as a variable of interest in future research on melodic dictation.
The third chapter of the dissertation discusses how aural skills pedagogy could incorporate methodologies from computational musicology in order to inform teaching practice. The chapter begins by establishing the degree to which aural skills pedagogues agree on the difficulty of melodies for melodic dictation using a survey representing 40 aural skills pedagogues. I then show how different sets of tools from computational musicology can approximate the intuitions of aural skills pedagogues using the survey data as a ground truth. The chapter concludes by putting forward a novel theory of musical memory– The Frequency Facilitation Hypothesis– which combines theoretical work from cognitive psychology and computational musicology. I show how this hypothesis can be applied in pedagogical settings to create a more linear path to success in the aural skills classroom for students.
In my fourth chapter, I introduce a novel corpus of 783 digitized melodies encoded in the
format (Huron 1994).
This chapter provides a rationale for the introduction of a new corpus in the broader context of computational musicology, contains a small corpus analysis in order to contextualize the new MeloSol corpus, and ends with a brief commentary regarding ontological and epistomological assumptions in music corpus research.
This dataset serves as a resource for future researchers in music, psychology, and the digital humanities.
In the fifth chapter, I synthesize the previous research in a melodic dictation experiment. Stimuli for the experiments are selected based on the symbolic features of the melodies discussed in earlier chapters and are manipulated as the independent variables. I then model responses from the experiments using both individual factors and musical features using mixed-effects modeling in order to predict how well an individual will perform in a melodic dictation. In discussing the results, I also note important caveats in scoring melodic dictation and highlight how changes in scoring can lead to changes in the final modeling. Results from this chapter will be discussed with reference to how findings are applicable to pedagogues in aural skills settings.
Finally, in my sixth chapter, I introduce a computational, cognitive model of melodic dictation with the goal of helping explain how students improve at melodic dictation. The model is based in research from both cognitive psychology (Cowan 2010) and computational musicology (Pearce 2018) and incorporates relevant theoretical aspects such as working memory (Chenette 2019; VanHandel, Wakefield, and Wilkins 2011) as well as the structure of the melody itself. In this chapter, I demonstrate how modeling the cognitive decision process during melodic dictation helps provide a precise framework for pedagogues to understand student’s inner cognition during melodic dictation and can help inform teaching practice.
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