2019
DOI: 10.1017/s026114301900028x
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Good things come in threes: triplet flow in recent hip-hop music

Abstract: MCs (rappers) such as Cardi B, Kendrick Lamar, Drake, Big Sean and Young Thug use triplet rhythms in their rapping, a practice that is known as triplet flow. This paper argues that the prevalence of triplet flow is one of the most aurally salient features of contemporary hip hop, and exemplifies the popularity and influence of the Atlanta-centred genre of trap music through its sparse, slow beats. Three types of triplet flow are defined – mixed, phrasal and total – and are used to explore how various songs and… Show more

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Cited by 12 publications
(4 citation statements)
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“…Although the feature fusion method improves the robustness of features, researchers ignore the relationship between features when selecting fusion features, and more low-level features are selected for fusion, which will produce a certain amount of feature information redundancy [ 9 ]. Duinker and others extracted human action shape information through Canny edge detection to represent action edge information and then achieved the purpose of human action recognition by matching similar edges [ 10 ]. They extended the traditional SFS (shape from silhouette) method, which is only suitable for static objects to objects with rigid body motion, further extended it to articulated objects to obtain the shape and motion information of various parts of the human body, and estimated the position of human joints by solving the simple motion constraint equation between articulated parts, so as to achieve the purpose of motion recognition.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Although the feature fusion method improves the robustness of features, researchers ignore the relationship between features when selecting fusion features, and more low-level features are selected for fusion, which will produce a certain amount of feature information redundancy [ 9 ]. Duinker and others extracted human action shape information through Canny edge detection to represent action edge information and then achieved the purpose of human action recognition by matching similar edges [ 10 ]. They extended the traditional SFS (shape from silhouette) method, which is only suitable for static objects to objects with rigid body motion, further extended it to articulated objects to obtain the shape and motion information of various parts of the human body, and estimated the position of human joints by solving the simple motion constraint equation between articulated parts, so as to achieve the purpose of motion recognition.…”
Section: Literature Reviewmentioning
confidence: 99%
“…For example, when dancing to a drum and bass track, people can embody the music as if the tempo is 160 beats per minute (bpm) or as if it is 80 bpm. In addition, triplets are an intrinsic part of the rhythms, as is easily perceived in dubstep or trap, for example (Duinker, 2019;Phillips, 2021). This stylistic signature is sometimes so strong, that the meter of those rhythms is perceived differently, not just the individual rhythmic elements in triplets.…”
Section: Meter and Music Genresmentioning
confidence: 99%
“…By and large, also in the listener-and listening-function-focussed area of mainstream popular music analysis, one can observe a trend towards the use of quantitative (computerised) measurement instruments. 24 However, recent studies concerned with a vast amount of songs from the UK or US charts in connection with notions of emotion and mood, for example, North et al (2018;2019a, b) and Krause and North (2020), skip the step of asking listeners directly for their experiences in favour of artificial intelligence processing. Some other quantitative studies utilising machine learning in the field of music emotion recognition integrate popular music listeners' perspectives to some extent in that they combine audio feature extraction with the retrieval of social, tag-based emotion annotations from services such as Last.fm (Song et al 2012;Jamdar et al 2015;Aljanaki et al 2017).…”
Section: Listener-based Analysismentioning
confidence: 99%