A computational exploration of melodic patterns in Arab-Andalusian music

Nuttall, Thomas; Casado, Miguel G.; Ferraro, Andrés; Conklin, Darrell; Repetto, Rafael Caro

doi:10.1080/17459737.2021.1917010

Cited by 4 publications

(6 citation statements)

References 12 publications

(12 reference statements)

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“…As noted by Nuttall et al (2019), many longer, rarer patterns (that occur infrequently across the whole dataset) have a disproportionately high importance as determined by each of our models, this in reality is a reflection of infrequency of occurrence, rather than correlation between occurrence and t . ab'.…”

Section: Minimum Frequency Of Occurrence Threshold (Mfo)mentioning

confidence: 70%

“…The task of pattern discovery within the context of Arab-Andalusian music is a much more recent development, with Nuttall et al (2019) taking advantage of the Arab-Andalusian corpus gathered as part of the CompMusic project to identify patterns characteristic of nawabāt. Since then, Chaachoo has published a new and revised version of his centonization theory (Chaachoo 2019), offering us the opportunity to explore this tradition from a new perspective with a wider range of methods.…”

Section: Previous Workmentioning

confidence: 99%

“…With the TF-IDF statistic as a measure of how characteristic each pattern is of each score we group scores by our t . ab' annotations and average the statistic for each pattern (as in Nuttall et al 2019). The result is a measure of how characteristic each pattern is of each t .…”

Section: Tf-idf Statisticmentioning

confidence: 99%

“…ab'. We consider the infrequency of such patterns as not providing us with enough signal to have confidence in this importance and like Nuttall et al (2019), impose a minimum frequency of occurrence (MFO) for each pattern per score per t . ab'.…”

Section: Minimum Frequency Of Occurrence Threshold (Mfo)mentioning

confidence: 99%

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A computational exploration of melodic patterns in Arab-Andalusian music

Nuttall,

Casado,

Ferraro

et al. 2023

Pattern in Music

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Here we present a computational approach to identifying melodic patterns in a dataset of 145 MusicXML scores with the aim of contributing to centonization theory in the Moroccan tradition of Arab-Andalusian Music -a theory in development by expert performer and researcher of this tradition, Amin Chaachoo. Central to his work is the definition of a set of characteristic patterns, or centos, for each t . ab', or melodic mode. We apply three methods: TF-IDF, Maximally General Distinctive Patterns (MGDP) and the Structure Induction Algorithm (SIA) to identify characteristic patterns at the level of t . ab'. A substantial number of the centos proposed by Chaachoo are identified and new melodic patterns are retrieved. A discussion with Chaachoo about the obtained results promoted the elicitation of other categories of recurrent patterns in the tradition different from the centos, contributing to a deeper musicological knowledge of the tradition.

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Section: Minimum Frequency Of Occurrence Threshold (Mfo)mentioning

confidence: 70%

Section: Previous Workmentioning

confidence: 99%

Section: Tf-idf Statisticmentioning

confidence: 99%

Section: Minimum Frequency Of Occurrence Threshold (Mfo)mentioning

confidence: 99%

See 2 more Smart Citations

A computational exploration of melodic patterns in Arab-Andalusian music

Nuttall,

Casado,

Ferraro

et al. 2023

Pattern in Music

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“…Prior and related to this algorithm, Conklin and Anagnostopoulou (2006) uses a sequential pattern mining method and works by constructing a suffix tree, which is then pruned based on the patterns' occurrences. MGDP and other related algorithm has been applied to a range of music styles, such as folk music and Gregorian chants (Conklin, 2013b;Conklin & Anagnostopoulou, 2011;Conklin & Maessen, 2019;Conklin & Weisser, 2016;Nuttall et al, 2021).…”

Section: Mgdpmentioning

confidence: 99%

Repetition sans Ennui

Ren

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Finding patterns is a common act in human intellectual endeavours, and it is a complex challenge tackled by both humans and algorithms. For several decades, musical pattern discovery algorithms have been researched, and researchers have been comparing human-annotated patterns to algorithmic outputs, as well as algorithms to algorithms. However, traditional metrics have not fully captured the rich insights that these comparisons could offer. To contribute to the comparisons between musical pattern discovery mechanisms, this dissertation spans seven chapters. Chapter 1 provides the background of the dissertation, including an overview, research approaches, contexts, scope, thesis statement, and an enumeration of contributions. Chapter 2 delves into the concept of musical patterns and explores the diverse landscape of musical pattern discovery algorithms. Our exploration reveals the complexities surrounding the definition of patterns and the multifaceted nature of these algorithms. Chapter 3 is dedicated to the collection tools for human-annotated musical patterns and the analysis of factors that influence annotations. We observe that musical background impacts annotated patterns; tool interfaces and automatic matching affect the length and frequency of annotations. Chapter 4 introduces four methods tailored for comparing musical pattern discovery algorithms. These methods provide novel insights into the discrepancies between human-annotated patterns and their algorithmically extracted counterparts. These methods provide a more comprehensive approach to comparing algorithms, aiding in the interpretation and evaluation of algorithmic outputs. Chapter 5 implements Pattrans, a Domain-Specific Language (DSL) in the functional language Haskell for comparing musical pattern occurrences through musical transformations. We delve into its design for uncovering the relations between pattern occurrences in a modular way. Chapter 6 employs Pattrans to scrutinise transformations between occurrences of musical patterns. Amongst other findings, we find that human-annotated patterns tend to have a higher proportion of exact repetitions and that different algorithms exhibit varying proportions of transformation compared to human annotations, contributing to a more nuanced view of pattern comparisons. In summary, this dissertation not only contributes fresh perspectives to the comparison of musical patterns, but also introduces methods and tools that enrich the field of musical pattern discovery. We examine the concept of musical pattern, conduct pattern annotation experiments, and visualise and analyse human-annotated and algorithmically extracted patterns. In addition, we recognise the potential of the musical transformations that lie behind repeating and varying pattern occurrences. Using Haskell, we model the relationship between patterns and transformations. Following this, we investigate how to employ transformations to relate and classify musical pattern occurrences. Throughout our journey, we advocate for a more comprehensive approach to pattern comparison, extending beyond traditional metrics.

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The Matrix Profile for Motif Discovery in Audio - An Example Application in Carnatic Music

Nuttall

Plaja-Roglans

Pearson

et al. 2023

Lecture Notes in Computer Science

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We present here a pipeline for the automated discovery of repeated motifs in audio. Our approach relies on state-of-the-art source separation, predominant pitch extraction and time series motif detection via the matrix profile. Owing to the appropriateness of this approach for the task of motif recognition in the Carnatic musical style of South India, and with access to the recently released Saraga Dataset of Indian Art Music, we provide an example application on a recording of a performance in the Carnatic rāga, R ītigaul . a, finding 56 distinct patterns of varying lengths that occur at least 3 times in the recording. The authors include a discussion of the potential musicological significance of this motif finding approach in relation to the particular tradition and beyond.

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A computational exploration of melodic patterns in Arab-Andalusian music

Cited by 4 publications

References 12 publications

A computational exploration of melodic patterns in Arab-Andalusian music

A computational exploration of melodic patterns in Arab-Andalusian music

Repetition sans Ennui

The Matrix Profile for Motif Discovery in Audio - An Example Application in Carnatic Music

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