A Reservoir-based Model for Human-like Perception of Complex Rhythm Pattern

• URL: http://arxiv.org/abs/2503.12509v1
• Date: Sun, 16 Mar 2025 14:02:42 GMT
• Title: A Reservoir-based Model for Human-like Perception of Complex Rhythm Pattern
• Authors: Zhongju Yuan, Geraint Wiggins, Dick Botteldooren,
• Abstract summary: We propose a hierarchical oscillator-based model to better understand the perception of complex musical rhythms in biological systems.<n>We evaluate the model using several representative rhythm patterns spanning the upper, middle, and lower bounds of human musical perception.<n>Our findings demonstrate that, while maintaining a high degree of synchronization accuracy, the model exhibits human-like rhythmic behaviours.
• Score: 4.7368661961661775
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Rhythm is a fundamental aspect of human behaviour, present from infancy and deeply embedded in cultural practices. Rhythm anticipation is a spontaneous cognitive process that typically occurs before the onset of actual beats. While most research in both neuroscience and artificial intelligence has focused on metronome-based rhythm tasks, studies investigating the perception of complex musical rhythm patterns remain limited. To address this gap, we propose a hierarchical oscillator-based model to better understand the perception of complex musical rhythms in biological systems. The model consists of two types of coupled neurons that generate oscillations, with different layers tuned to respond to distinct perception levels. We evaluate the model using several representative rhythm patterns spanning the upper, middle, and lower bounds of human musical perception. Our findings demonstrate that, while maintaining a high degree of synchronization accuracy, the model exhibits human-like rhythmic behaviours. Additionally, the beta band neuronal activity in the model mirrors patterns observed in the human brain, further validating the biological plausibility of the approach.

Related papers

• Mode-conditioned music learning and composition: a spiking neural network inspired by neuroscience and psychology [5.2419221159594676]
  We propose a spiking neural network inspired by brain mechanisms and psychological theories to represent musical modes and keys.<n>Our research aims to create a system that not only learns and generates music but also bridges the gap between human cognition and artificial intelligence.
  arXiv  Detail & Related papers  (2024-11-22T07:29:26Z)
• Artificial Kuramoto Oscillatory Neurons [65.16453738828672]
  It has long been known in both neuroscience and AI that ''binding'' between neurons leads to a form of competitive learning.<n>We introduce Artificial rethinking together with arbitrary connectivity designs such as fully connected convolutional, or attentive mechanisms.<n>We show that this idea provides performance improvements across a wide spectrum of tasks such as unsupervised object discovery, adversarial robustness, uncertainty, and reasoning.
  arXiv  Detail & Related papers  (2024-10-17T17:47:54Z)
• A Dynamic Systems Approach to Modelling Human-Machine Rhythm Interaction [4.33608942673382]
  This study introduces a computational model inspired by the physical and biological processes underlying rhythm processing. Our findings demonstrate the model's ability to accurately perceive and adapt to rhythmic patterns within the human perceptible range.
  arXiv  Detail & Related papers  (2024-06-26T10:07:20Z)
• Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks [59.38765771221084]
  We present a physiologically inspired speech recognition architecture compatible and scalable with deep learning frameworks. We show end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronising neural activity to improve recognition performance.
  arXiv  Detail & Related papers  (2024-04-22T09:40:07Z)
• MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints [50.61346764110482]
  We integrate a musculoskeletal system with a learnable parametric hand model, MANO, to create MS-MANO. This model emulates the dynamics of muscles and tendons to drive the skeletal system, imposing physiologically realistic constraints on the resulting torque trajectories. We also propose a simulation-in-the-loop pose refinement framework, BioPR, that refines the initial estimated pose through a multi-layer perceptron network.
  arXiv  Detail & Related papers  (2024-04-16T02:18:18Z)
• Sigma-lognormal modeling of speech [0.0]
  This work presents a speech kinematics based model that can be used to study, analyze, and complex speech kinematics in a simplified manner. A method based on the kinematic theory of rapid human movements and its associated Sigma lognormal model are applied to describe the impulse response of the neuromuscular networks involved in speech. Experiments carried out with the (English) VTR TIMIT database and the (German) Saarbrucken Voice Database, including people of different ages, corroborate the link between the extracted parameters and aging.
  arXiv  Detail & Related papers  (2024-01-27T18:00:20Z)
• DiffDance: Cascaded Human Motion Diffusion Model for Dance Generation [89.50310360658791]
  We present a novel cascaded motion diffusion model, DiffDance, designed for high-resolution, long-form dance generation. This model comprises a music-to-dance diffusion model and a sequence super-resolution diffusion model. We demonstrate that DiffDance is capable of generating realistic dance sequences that align effectively with the input music.
  arXiv  Detail & Related papers  (2023-08-05T16:18:57Z)
• Persistent-Transient Duality: A Multi-mechanism Approach for Modeling Human-Object Interaction [58.67761673662716]
  Humans are highly adaptable, swiftly switching between different modes to handle different tasks, situations and contexts. In Human-object interaction (HOI) activities, these modes can be attributed to two mechanisms: (1) the large-scale consistent plan for the whole activity and (2) the small-scale children interactive actions that start and end along the timeline. This work proposes to model two concurrent mechanisms that jointly control human motion.
  arXiv  Detail & Related papers  (2023-07-24T12:21:33Z)
• Modeling the Repetition-based Recovering of Acoustic and Visual Sources with Dendritic Neurons [5.306881553301636]
  In natural auditory environments, acoustic signals originate from the temporal superimposition of different sound sources. Experiments on humans have demonstrated that the auditory system can identify sound sources as repeating patterns embedded in the acoustic input. We propose a biologically inspired computational model to perform blind source separation on sequences of mixtures of acoustic stimuli.
  arXiv  Detail & Related papers  (2022-01-16T19:35:59Z)
• On the Evolution of Neuron Communities in a Deep Learning Architecture [0.7106986689736827]
  This paper examines the neuron activation patterns of deep learning-based classification models. We show that both the community quality (modularity) and entropy are closely related to the deep learning models' performances.
  arXiv  Detail & Related papers  (2021-06-08T21:09:55Z)
• Continuous Learning and Adaptation with Membrane Potential and Activation Threshold Homeostasis [91.3755431537592]
  This paper presents the Membrane Potential and Activation Threshold Homeostasis (MPATH) neuron model. The model allows neurons to maintain a form of dynamic equilibrium by automatically regulating their activity when presented with input. Experiments demonstrate the model's ability to adapt to and continually learn from its input.
  arXiv  Detail & Related papers  (2021-04-22T04:01:32Z)
• Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning [55.854205371307884]
  We formalize the music-conditioned dance generation as a sequence-to-sequence learning problem. We propose a novel curriculum learning strategy to alleviate error accumulation of autoregressive models in long motion sequence generation. Our approach significantly outperforms the existing state-of-the-arts on automatic metrics and human evaluation.
  arXiv  Detail & Related papers  (2020-06-11T00:08:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.