midiVERTO: A Web Application to Visualize Tonality in Real Time

Harasim, Daniel; Affatato, Giovanni; Moss, Fabian C.

doi:10.1007/978-3-031-07015-0_31

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…We also show that applying the discrete Fourier transform (DFT) on pitch-class vectors produces an effective map of keys and harmonic functions. This method has previously been used to characterize musical keys (Cuddy and Badertscher, 1987;Krumhansl, 1990;Yust, 2017b), as a feature description for musical audio (Harte et al, 2006;Ramiréz et al, 2020), and as a tool for computational analysis of musical works and large corpora (Yust, 2019;Novarro-Cáceres et al, 2020;Harding, 2020Harding, , 2021Chiu, 2021;Viaccoz et al, 2022;Harasim et al, 2022). In our Experiment 1 we use the DFT to classify clusters into three basic types (triadic, tetradic, and scalar).…”

Section: Introductionmentioning

confidence: 99%

A Clustering-Based Approach to Automatic Harmonic Analysis: An Exploratory Study of Harmony and Form in Mozart’s Piano Sonatas

Yust

Lee

Pinsky

2022

Transactions of the International Society for Music Information Retrieval

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We implement a novel approach to automatic harmonic analysis using a clustering method on pitch-class vectors (chroma vectors). The advantage of this method is its lack of top-down assumptions, allowing us to objectively validate the basic music theory premise of a chord lexicon consisting of triads and seventh chords, which is presumed by most research in automatic harmonic analysis. We use the discrete Fourier transform and hierarchical clustering to analyse features of the clustering solutions and illustrate associations between the features and the distribution of clusters over sections of the sonata forms. We also analyse the transition matrix, recovering elements of harmonic function theory.

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Section: Introductionmentioning

confidence: 99%

A Clustering-Based Approach to Automatic Harmonic Analysis: An Exploratory Study of Harmony and Form in Mozart’s Piano Sonatas

Yust

Lee

Pinsky

2022

Transactions of the International Society for Music Information Retrieval

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“…Each point Φ 𝑤 (𝑡 ) on this curve corresponds to a particular segment [𝑠, 𝑠 + 𝑤 ] with starting point 𝑠 and pitch-class content 𝑃 (𝑠, 𝑤); see Section 3 for details. Two example segments [𝑠 1 , 𝑠 a new concept for music analysis, was enabled through the web application midiVERTO 1 for analyzing and visualizing tonal structures in pieces of music [5]. The dynamic nature of the application enables the exploration and comparison of different analyses by changing the parameters through a visual interface.…”

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confidence: 99%

Phantom Curves

Moss

Affatato

Harasim

2022

9th International Conference on Digital Libraries for Musicology

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Scientific discovery can arise by many means. Results can be obtained from meticulous methodological rigor and carefully plannedthrough experiments, but oftentimes insights are also gained through spontaneous associations of previously unrelated facts or exploratory work. In particular for the latter mode of discovery, a stimulating environment is quintessential in order to allow creativity to flow [4,9]. Collaborative inter-and transdisciplinary research for which knowledge and expertise from different fields is brought into interaction, is especially likely to provide such environments. Interdisciplinary work can, however, be impeded by specialized expert knowledge that is difficult to communicate to researchers with different backgrounds. In such situations, appropriately designed tools may help to build bridges between different research traditions and provide interfaces to otherwise restricted knowledge.Here, we report an instance of scientific discovery made possible through interactive exploration using an online interface for music visualization. We document how the discovery of phantom curves,

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