A dynamic Markov model for n<sup>th</sup>-order movement prediction

Cornelius, Ian; Shuttleworth, James; Taramonli, Sandy

doi:10.1109/icsai.2017.8248331

Cited by 1 publication

(1 citation statement)

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“…There is no straightforward means of estimating the corresponding TPs or of making use of a proxy, as is done by TRACX2, based on the proximity between intervals, a property that is not part of a simple first-order Markov model using TPs. The use of more sophisticated Markov models (e.g., dynamic n-order Markov models, Cornelius, Shuttleworth, & Taramonli, 2017) is, however, beyond the scope of this paper.…”

Section: First-order Markov Modelsmentioning

confidence: 99%

A Recurrent Connectionist Model of Melody Perception: An Exploration Using TRACX2

Defays¹,

French

Tillmann

2023

Cognitive Science

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Are similar, or even identical, mechanisms used in the computational modeling of speech segmentation, serial image processing, and music processing? We address this question by exploring how TRACX2, a recognition‐based, recursive connectionist autoencoder model of chunking and sequence segmentation, which has successfully simulated speech and serial‐image processing, might be applied to elementary melody perception. The model, a three‐layer autoencoder that recognizes “chunks” of short sequences of intervals that have been frequently encountered on input, is trained on the tone intervals of melodically simple French children's songs. It dynamically incorporates the internal representations of these chunks into new input. Its internal representations cluster in a manner that is consistent with “human‐recognizable” melodic categories. TRACX2 is sensitive to both contour and proximity information in the musical chunks that it encounters in its input. It shows the “end‐of‐word” superiority effect demonstrated by Saffran et al. (1999) for short musical phrases. The overall findings suggest that the recursive autoassociative chunking mechanism, as implemented in TRACX2, may be a general segmentation and chunking mechanism, underlying not only word‐ and image‐chunking, but also elementary melody processing.

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Section: First-order Markov Modelsmentioning

confidence: 99%