Fractal Tempo Fluctuation and Pulse Prediction

Rankin, Summer K.; Large, Edward W.; Fink, Philip W.

doi:10.1525/mp.2009.26.5.401

Cited by 77 publications

(75 citation statements)

References 47 publications

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“…Listeners significantly prefer music mirroring long-range correlated temporal deviations over uncorrelated (white noise) fluctuations (5,6). LRC are also inherent in the reproduction of both spatial and temporal intervals of single subjects (4,(7)(8)(9) and in musical compositions, such as pitch fluctuations (a simple example of pitch fluctuations is a melody) (10,11) and note lengths (12). The observation of power law correlations in fluctuations of pitch and note length in compositions reflects a hierarchical, selfsimilar structure in these compositions.…”

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confidence: 99%

Synchronization in human musical rhythms and mutually interacting complex systems

Hennig

2014

Proc. Natl. Acad. Sci. U.S.A.

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Though the music produced by an ensemble is influenced by multiple factors, including musical genre, musician skill, and individual interpretation, rhythmic synchronization is at the foundation of musical interaction. Here, we study the statistical nature of the mutual interaction between two humans synchronizing rhythms. We find that the interbeat intervals of both laypeople and professional musicians exhibit scale-free (power law) cross-correlations. Surprisingly, the next beat to be played by one person is dependent on the entire history of the other person's interbeat intervals on timescales up to several minutes. To understand this finding, we propose a general stochastic model for mutually interacting complex systems, which suggests a physiologically motivated explanation for the occurrence of scale-free cross-correlations. We show that the observed long-term memory phenomenon in rhythmic synchronization can be imitated by fractal coupling of separately recorded or synthesized audio tracks and thus applied in electronic music. Though this study provides an understanding of fundamental characteristics of timing and synchronization at the interbrain level, the mutually interacting complex systems model may also be applied to study the dynamics of other complex systems where scale-free crosscorrelations have been observed, including econophysics, physiological time series, and collective behavior of animal flocks.time series analysis | long-range cross-correlations | anticorrelations | musical coupling | interbrain synchronization I n his book Musicophilia, neurologist Oliver Sacks writes: "In all societies, a primary function of music is collective and communal, to bring and bind people together. People sing together and dance together in every culture, and one can imagine them having done so around the first fires, a hundred thousand years ago" (1). Sacks adds, "In such a situation, there seems to be an actual binding of nervous systems accomplished by rhythm" (2). These thoughts lead to the question: Is there any underlying and quantifiable structure to the subjective experience of "musical binding"? Here, we examine the statistical nature of musical binding (also referred to as musical coupling) when two humans play rhythms in synchrony.Every beat a single (noninteracting) layperson or musician plays is accompanied by small temporal deviations from the exact beat pattern, i.e., even a trained musician will hit a drum beat slightly ahead or behind the metronome (with a SD of typically 5-15 ms). Interestingly, these deviations are statistically dependent and exhibit long-range correlations (LRC) (3, 4). Listeners significantly prefer music mirroring long-range correlated temporal deviations over uncorrelated (white noise) fluctuations (5, 6). LRC are also inherent in the reproduction of both spatial and temporal intervals of single subjects (4, 7-9) and in musical compositions, such as pitch fluctuations (a simple example of pitch fluctuations is a melody) (10, 11) and note lengths (12). The observation of ...

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confidence: 99%

Synchronization in human musical rhythms and mutually interacting complex systems

Hennig

2014

Proc. Natl. Acad. Sci. U.S.A.

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“…R ranges from 0 (no stability or no entrainment) to 1 (perfect stability or perfect entrainment), and it has previously been used in quantifying individual timing stability and especially synchronicity and entrainment in dyadic and group timing (Himberg, 2006(Himberg, , 2014Rankin et al, 2009;Lucas et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

Turn-Taking in Human Communicative Interaction

Holler

Kendrick

Casillas

et al. 2016

Frontiers Research Topics

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The core use of language is in face-to-face conversation. This is characterized by rapid turn-taking. This turn-taking poses a number central puzzles for the psychology of language. Consider, for example, that in large corpora the gap between turns is on the order of 100 to 300 ms, but the latencies involved in language production require minimally between 600ms (for a single word) or 1500 ms (for as simple sentence). This implies that participants in conversation are predicting the ends of the incoming turn and preparing in advance. But how is this done? What aspects of this prediction are done when? What happens when the prediction is wrong? What stops participants coming in too early? If the system is running on prediction, why is there consistently a mode of 100 to 300 ms in response time? The timing puzzle raises further puzzles: it seems that comprehension must run parallel with the preparation for production, but it has been presumed that there are strict cognitive limitations on more than one central process running at a time. How is this bottleneck overcome? Far from being 'easy' as some psychologists have suggested, conversation may be one of the most demanding cognitive tasks in our everyday lives. Further questions naturally arise: how do children learn to master this demanding task, and what is the developmental trajectory in this domain? Research shows that aspects of turn-taking such as its timing are remarkably stable across languages and cultures, but the word order of languages varies enormously. How then does prediction of the incoming turn work when the verb (often the informational nugget in a clause) is at the end? Conversely, how can production work fast enough in languages that have the verb at the beginning, thereby requiring early planning of the whole clause? What happens when one changes modality, as in sign languages -- with the loss of channel constraints is turn-taking much freer? And what about face-to-face communication amongst hearing individuals -- do gestures, gaze, and other body behaviors facilitate turn-taking? One can also ask the phylogenetic question: how did such a system evolve? There seem to be parallels (analogies) in duetting bird species, and in a variety of monkey species, but there is little evidence of anything like this among the great apes. All this constitutes a neglected set of problems at the heart of the psychology of language and of the language sciences. This research topic welcomes contributions from right across the board, for example from psycholinguists, developmental psychologists, students of dialogue and conversation analysis, linguists interested in the use of language, phoneticians, corpus analysts and comparative ethologists or psychologists. We welcome contributions of all sorts, for example original research papers, opinion pieces, and reviews of work in subfields that may not be fully understood in other subfields

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“…It is well known that humans can successfully identify metre and follow the tempo based off such an expressive rhythm (Epstein, 1995). Rankin et al (2009) conducted a recent study on human beat induction and found that subjects were able to adapt to relatively large fluctuations in tempo resulting from performances of piano music in various genres. The participants could successfully find a pulse at the crotchet (quarter note) or quaver (8 note) metrical level.…”

Section: Expressive Rhythmmentioning

confidence: 99%

Generating Time: Rhythmic Perception, Prediction and Production with Recurrent Neural Networks

Elmsley

Weyde

Armstrong

2017

Journal of Creative Music Systems

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In the quest for a convincing musical agent that performs in real time alongside human performers, the issues surrounding expressively timed rhythm must be addressed. Current beat tracking methods are not sufficient to follow rhythms automatically when dealing with varying tempo and expressive timing. In the generation of rhythm, some existing interactive systems ignore the pulse entirely, or fix a tempo after some time spent listening to input. Since music unfolds in time, we take the view that musical timing needs to be at the core of a music generation system. Our research explores a connectionist machine learning approach to expressive rhythm generation, based on cognitive and neurological models. Two neural network models are combined within one integrated system. A Gradient Frequency Neural Network (GFNN) models the perception of periodicities by resonating nonlinearly with the musical input, creating a hierarchy of strong and weak oscillations that relate to the metrical structure. A Long Short-term Memory Recurrent Neural Network (LSTM) models longer-term temporal relations based on the GFNN output.The output of the system is a prediction of when in time the next rhythmic event is likely to occur. These predictions can be used to produce new rhythms, forming a generative model.We have trained the system on a dataset of expressively performed piano solos and evaluated its ability to accurately predict rhythmic events. Based on the encouraging results, we conclude that the GFNN-LSTM model has great potential to add the ability to follow and generate expressive rhythmic structures to real-time interactive systems.

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Fractal Tempo Fluctuation and Pulse Prediction

Cited by 77 publications

References 47 publications

Synchronization in human musical rhythms and mutually interacting complex systems

Synchronization in human musical rhythms and mutually interacting complex systems

Turn-Taking in Human Communicative Interaction

Generating Time: Rhythmic Perception, Prediction and Production with Recurrent Neural Networks

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