MeloSol Corpus

Dj, Baker

doi:10.18061/emr.v16i1.7645

Cited by 3 publications

(3 citation statements)

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“…In order to evaluate the model and our results, we compare the model's output to data from two sources. The first is a dataset from (n = 41) aural skills pedagogues who responded to a survey asking respondents to rate the difficulty of 20 melodies taken from the 5th Edition Berkowitz (Berkowitz et al, 2011), now digitized as The MeloSol Corpus (Baker, 2021). The second source draws from data (n = 40) looking to model responses to a melodic dictation experiment that used standardized scoring procedures, validated with high interrater reliability, and measures of the individual's cognitive abilities such as their working memory capacity detailed in (Baker, D, Monzingo, E., Shanahan, D., 2018).…”

Section: Discussionmentioning

confidence: 99%

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A Computational Model of Melodic Dictation

Dj¹

2022

Preprint

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Research on melodic dictation within aural skills pedagogy literature is primarily didactic (Karpinski, 1990; 2000); it seeks to describe strategies listeners might adopt to learn how to do this task. Absent from the aural skills literature on melodic dictation is a cognitive model which posits the components of this process and how they might function. In order to arrive at a more thorough understanding of melodic dictation–and consequently melodic recall–we introduce and assess the performance of a computational model of melodic memory.The model takes a melody as input, as well as parameters that estimates a listener’s working memory and long-term memory and outputs a difficulty score. To fit and assess the model, we compare the predictions of this computational model to both subjective ratings of melodic dictation difficulty of twenty melodies from active aural skills pedagogues (n = 41) and empirical data collected from individuals (n = 40) who completed four melodic dictations. To contextualize the model’s performance, we also compare its predictions against several computationally extracted features (Müllensiefen, 2009). Evidence from the model’s slightly superior performance provides evidence demonstrating how information content models (Pearce, 2005; 2018) can be applied within melodic memory research.

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Section: Discussionmentioning

confidence: 99%

“…A visualization of this is given in Figure 3. Here, we plot the distribution of 1-grams through 10-grams scale degree tokens from the MeloSol corpus (Baker, 2021) using the Humdrum toolkit (Huron, 1994). As noted by Gustar (2019), distributions of n-grams in a corpus approximate a power law distribution.…”

Section: Working and Long Term Memorymentioning

confidence: 99%

A Computational Model of Melodic Dictation

Dj¹

2022

Preprint

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“…Recent developments to the related experimental approach of melodic discrimination testing via explanatory item response theory (Harrison et al, 2017;Harrison et al, 2016) and usage of large-scale musical corpora (e.g., Baker, 2021;Pfleiderer et al, 2017) have bolstered and improved some of these aspects of the melodic recognition paradigm. However, the so-called melodic recall paradigm, employed in this study, offers a different kind of insight into the different musical dimensions retained in memory.…”

Section: Melodic Recognition Paradigmmentioning

confidence: 99%

Learning and Recalling Melodies

Silas,

Müllensiefen

2023

Music Perception: An Interdisciplinary Journal

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Using melodic recall paradigm data, we describe an algorithmic approach to assessing melodic learning across multiple attempts. In a first simulation experiment, we reason for using similarity measures to assess melodic recall performance over previously utilized accuracy-based measures. In Experiment 2, with up to six attempts per melody, 31 participants sang back 28 melodies (length 15–48 notes) presented either as a piano sound or a vocal audio excerpt from real pop songs. Our analysis aimed to predict the similarity between the target melody and participants’ sung recalls across successive attempts. Similarity was measured with different algorithmic measures reflecting various structural (e.g., tonality, intervallic) aspects of melodies and overall similarity. However, previous melodic recall research mentioned, but did not model, that the length of the sung recalls tends to increase across attempts, alongside overall performance. Consequently, we modeled how the attempt length changes alongside similarity to meet this omission in the literature. In a mediation analysis, we find that a target melody’s length, but not other melodic features, is the main predictor of similarity via the attempt length. We conclude that sheer length constraints appear to be the main factor when learning melodies long enough to require several attempts to recall. Analytical features of melodic structure may be more important for shorter melodies, or with stimulus sets that are structurally more diverse than those found in the sample of pop songs used in this study.

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Singing Ability Assessment: Development and validation of a singing test based on item response theory and a general open-source software environment for singing data

Silas,

Müllensiefen,

Kopiez

2023

Behav Res

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We describe the development of the Singing Ability Assessment (SAA) open-source test environment. The SAA captures and scores different aspects of human singing ability and melodic memory in the context of item response theory. Taking perspectives from both melodic recall and singing accuracy literature, we present results from two online experiments (N = 247; N = 910). On-the-fly audio transcription is produced via a probabilistic algorithm and scored via latent variable approaches. Measures of the ability to sing long notes indicate a three-dimensional principal components analysis solution representing pitch accuracy, pitch volatility and changes in pitch stability (proportion variance explained: 35%; 33%; 32%). For melody singing, a mixed-effects model uses features of melodic structure (e.g., tonality, melody length) to predict overall sung melodic recall performance via a composite score [R2c = .42; R2m = .16]. Additionally, two separate mixed-effects models were constructed to explain performance in singing back melodies in a rhythmic [R2c = .42; R2m = .13] and an arhythmic [R2c = .38; R2m = .11] condition. Results showed that the yielded SAA melodic scores are significantly associated with previously described measures of singing accuracy, the long note singing accuracy measures, demographic variables, and features of participants’ hardware setup. Consequently, we release five R packages which facilitate deploying melodic stimuli online and in laboratory contexts, constructing audio production tests, transcribing audio in the R environment, and deploying the test elements and their supporting models. These are published as open-source, easy to access, and flexible to adapt.

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MeloSol Corpus

Cited by 3 publications

References 4 publications

A Computational Model of Melodic Dictation

A Computational Model of Melodic Dictation

Learning and Recalling Melodies

Singing Ability Assessment: Development and validation of a singing test based on item response theory and a general open-source software environment for singing data

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