From beat tracking to beat expectation: Cognitive-based beat tracking for capturing pulse clarity through time

Miguel, Martín A.; Sigman, Mariano; Slezak, Diego Fernández

doi:10.1371/journal.pone.0242207

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…This problem might be partially addressed by incorporating a model that evaluates multiple distinct hypotheses for beat or meter (e.g. [ 70 , 71 ], or [ 72 ] with appropriate probabilistic interpretation) as an additional level of inference in parallel with ongoing phase and tempo inference.…”

Section: Discussionmentioning

confidence: 99%

Expectancy-based rhythmic entrainment as continuous Bayesian inference

Cannon

2021

PLoS Comput Biol

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When presented with complex rhythmic auditory stimuli, humans are able to track underlying temporal structure (e.g., a “beat”), both covertly and with their movements. This capacity goes far beyond that of a simple entrained oscillator, drawing on contextual and enculturated timing expectations and adjusting rapidly to perturbations in event timing, phase, and tempo. Previous modeling work has described how entrainment to rhythms may be shaped by event timing expectations, but sheds little light on any underlying computational principles that could unify the phenomenon of expectation-based entrainment with other brain processes. Inspired by the predictive processing framework, we propose that the problem of rhythm tracking is naturally characterized as a problem of continuously estimating an underlying phase and tempo based on precise event times and their correspondence to timing expectations. We present two inference problems formalizing this insight: PIPPET (Phase Inference from Point Process Event Timing) and PATIPPET (Phase and Tempo Inference). Variational solutions to these inference problems resemble previous “Dynamic Attending” models of perceptual entrainment, but introduce new terms representing the dynamics of uncertainty and the influence of expectations in the absence of sensory events. These terms allow us to model multiple characteristics of covert and motor human rhythm tracking not addressed by other models, including sensitivity of error corrections to inter-event interval and perceived tempo changes induced by event omissions. We show that positing these novel influences in human entrainment yields a range of testable behavioral predictions. Guided by recent neurophysiological observations, we attempt to align the phase inference framework with a specific brain implementation. We also explore the potential of this normative framework to guide the interpretation of experimental data and serve as building blocks for even richer predictive processing and active inference models of timing.

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Section: Discussionmentioning

confidence: 99%

Expectancy-based rhythmic entrainment as continuous Bayesian inference

Cannon

2021

PLoS Comput Biol

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“…Beat perception is a high-level cognitive percept that cannot be directly extracted using music information retrieval approaches that use automated signal processing based mainly on one or more acoustic features (e.g., Lartillot, Eerola, Toiviainen, & Fornari, 2008;McKinney, Moelants, Davies, & Klapuri, 2007). Even when applying state-of-the art multi-model approaches, the models' output does not match human performance (except only under certain conditions), and their performance is significantly influenced by the musical style (Böck, Krebs, & Widmer, 2014) or beat interpretations of the rhythm (Miguel, Sigman, & Fernandez Slezak, 2020). We therefore, turned to experts-based annotation to index this percept (Figure 1).…”

Section: Annotation Of Temporal Predictabilitymentioning

confidence: 99%

Feeling the Beat: Temporal Predictability is Associated with Ongoing Changes in Music-Induced Pleasantness

Singer

Jacoby

Hendler

et al. 2023

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Music is a complex phenomenon that elicits a range of emotional responses, influenced by numerous variables, such as rhythm, melody and harmony. One interesting aspect of music is listeners’ ability to predict its continuation as it unfolds – an inherent attribute hypothesized to contribute to our emotional response to music. In this study, we investigated this link by examining the relationship between temporal predictability – the ability to predict the timing of the next event – and the ongoing changes in music-induced pleasantness. Temporal predictability was operationalized as the degree to which taps of 20 musically trained participants, who tapped to the beat along three naturalistic and highly contrastive musical pieces, were aligned. We then examined the degree to which this measure could explain the ongoing emotional experience, as reflected in continuous measures of arousal and valence, in a separate group of 40 participants that listened to these pieces. Our findings reveal a positive correlation between fluctuations in reported valence and temporal predictability, even when controlling for a set of other musical features, in four out of five musical sections. The only exception being a lyrical slow section. These findings were further supported by a large online database of annotated musical emotions (n = 1780 songs), where a consistent and robust correlation between valence ratings and an automatically extracted feature of pulse clarity was demonstrated. Overall, our findings shed light on the significance of temporal predictability as a contributing factor to the hedonic experience of music, especially within the tempo range of salient beat perception.

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“…In particular, one of the most significant and challenging tasks is that of time signature inference and beat tracking, especially in metrically ambiguous extracts, a common occurrence across genres [1]- [3]. Inaccurate beat tracking for performances with rubato (time-varying tempo) or multiple metric interpretations, regardless of the accuracy of note detections, will result in poor quality transcriptions given the misalignment of key rhythmic structures in the transcription [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…Likewise, for methods that tackle time-varying time signatures using HMMs [10], [16] or RNNS [24], [39], the models employ cascade formats, such that joint beat and bar tracking is performed sequentially resulting in beat lengths independent of metric properties. The subjectivity of perceived pulse (beat) positions and lengths is addressed in literature through the employment of agents [5], [31], yet the process has not been generalised to joint time signature and beat tracking tasks. In general, the majority of algorithms assume either a constant tempo, beat length, or time signature division over time, providing methods for extracting each independently, and thus poor performance is reported for extracts with rubato and irregular beat and time signature changes [25].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Time Signature Recognition, Tempo Inference, and Beat Tracking Through the Metrogram Transform

Cozens,

Godsill

2024

IEEE Open J. Signal Process.

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This paper proposes a probabilistic approach for extracting time-varying and irregular time signature information from polyphonic audio extracts, subsequently providing beat and bar line positions given inferred time signature divisions. This is achieved via dynamically evaluating the beat tempo as a function of time through finding an optimal compromise in beat and bar alignment in the time and tempo domains. Time signature divisions are determined based on a new representation, termed the Metrogram, that presents time-varying information regarding rhythmic and metric periodicities in the Tempogram. Our methodology is characterised by its ability to provide a distribution over metric interpretations, offering insights into the diverse ways music can be rhythmically perceived. Results indicate high-level accuracy for a variety of polyphonic extracts containing irregular, complex, irrational, and time-varying time signatures. Accuracy rivalling state-of-the-art methodologies is also reported in a beat tracking task performed on the standard Ballroom Dataset. The paper offers insights into the field of dynamic time signature recognition and beat tracking, offering a valuable and versatile resource for the analysis, composition, and performance of music.

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From beat tracking to beat expectation: Cognitive-based beat tracking for capturing pulse clarity through time

Cited by 5 publications

References 30 publications

Expectancy-based rhythmic entrainment as continuous Bayesian inference

Expectancy-based rhythmic entrainment as continuous Bayesian inference

Feeling the Beat: Temporal Predictability is Associated with Ongoing Changes in Music-Induced Pleasantness

Dynamic Time Signature Recognition, Tempo Inference, and Beat Tracking Through the Metrogram Transform

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