Measuring rhythmic complexity: A primer to quantify and compare temporal structure in speech, movement, and animal vocalizations

Ravignani, Andrea; Norton, Philipp

doi:10.1093/jole/lzx002

Cited by 44 publications

(77 citation statements)

References 53 publications

(74 reference statements)

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“…In these situations, an alternative is to annotate the recordings with the onset or offset times and investigate the temporal structure of these events . In this section, we present another method (in addition to the AF analyses above and other techniques described in detail elsewhere) for characterizing and comparing temporal patterns in a series of events that was recently proposed in Ref. .…”

Section: Techniques For Quantifying Rhythmic Structurementioning

confidence: 99%

Rhythm in speech and animal vocalizations: a cross‐species perspective

Ravignani

Bella

Falk

et al. 2019

Annals of the New York Academy of Sciences

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Why does human speech have rhythm? As we cannot travel back in time to witness how speech developed its rhythmic properties and why humans have the cognitive skills to process them, we rely on alternative methods to find out. One powerful tool is the comparative approach: studying the presence or absence of cognitive/behavioral traits in other species to determine which traits are shared between species and which are recent human inventions. Vocalizations of many species exhibit temporal structure, but little is known about how these rhythmic structures evolved, are perceived and produced, their biological and developmental bases, and communicative functions. We review the literature on rhythm in speech and animal vocalizations as a first step toward understanding similarities and differences across species. We extend this review to quantitative techniques that are useful for computing rhythmic structure in acoustic sequences and hence facilitate cross‐species research. We report links between vocal perception and motor coordination and the differentiation of rhythm based on hierarchical temporal structure. While still far from a complete cross‐species perspective of speech rhythm, our review puts some pieces of the puzzle together.

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Section: Techniques For Quantifying Rhythmic Structurementioning

confidence: 99%

Rhythm in speech and animal vocalizations: a cross‐species perspective

Ravignani

Bella

Falk

et al. 2019

Annals of the New York Academy of Sciences

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“…Where we speak of rhythms in animals' acoustic signals a musicologist might only talk about different beats and tempi. What we mean in this paper with rhythm and the connotation of rhythm used in other studies on the subject [4,5,10] describes a temporal structure that might have varying complexity but is mostly based on an isochronous beat (i.e. sounds produced by a metronome).…”

Section: Introductionmentioning

confidence: 99%

Comparison of methods for rhythm analysis of complex animals’ acoustic signals

Burchardt

Knörnschild

2020

PLoS Comput Biol

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Analyzing the rhythm of animals' acoustic signals is of interest to a growing number of researchers: evolutionary biologists want to disentangle how these structures evolved and what patterns can be found, and ecologists and conservation biologists aim to discriminate cryptic species on the basis of parameters of acoustic signals such as temporal structures. Temporal structures are also relevant for research on vocal production learning, a part of which is for the animal to learn a temporal structure. These structures, in other words, these rhythms, are the topic of this paper. How can they be investigated in a meaningful, comparable and universal way? Several approaches exist. Here we used five methods to compare their suitability and interpretability for different questions and datasets and test how they support the reproducibility of results and bypass biases. Three very different datasets with regards to recording situation, length and context were analyzed: two social vocalizations of Neotropical bats (multisyllabic, medium long isolation calls of Saccopteryx bilineata, and monosyllabic, very short isolation calls of Carollia perspicillata) and click trains of sperm whales, Physeter macrocephalus. Techniques to be compared included Fourier analysis with a newly developed goodness-of-fit value, a generate-and-test approach where data was overlaid with varying artificial beats, and the analysis of inter-onset-intervals and calculations of a normalized Pairwise Variability Index (nPVI). We discuss the advantages and disadvantages of the methods and we also show suggestions on how to best visualize rhythm analysis results. Furthermore, we developed a decision tree that will enable researchers to select a suitable and comparable method on the basis of their data. Author summaryIn the analysis of animal communication more and more interest is shown in rhythm of animal communication and what information this might convey. In this paper, we establish a workflow to analyze the temporal structure-namely the rhythm-of any particular animals'acoustic signal with methods that are applicable for a wide range of signals and results that are easily comparable and interpretable. This workflow will enhance the understanding of rhythmicality in animals' acoustic signals as well as facilitate comparison between species. Methods we conducted ranged from simple distributional and visual PLOS COMPUTATIONAL BIOLOGY PLOS Computational Biology | https://doi.

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“…Finally, new methods to model (van der Weij et al, 2017) and measure rhythmicity have been proposed, either quantitatively from data (Daniele, 2017;Malisz et al, 2017;Ravignani, 2017b;Ravignani and Norton, 2017) or as a test battery on human participants (Dalla Bella et al, 2016).…”

Section: Measuring Rhythmmentioning

confidence: 99%