Nonspecific hebbian neural network model predicts musical scales discreteness and just intonation without using octave-equivalency mapping.

ABSTRACT: This study continues investigating the consonance-pattern emerging neural network model introduced in our previous publication, specifically to test if it will reproduce the results using 100-fold finer precision of 1/100th of a semitone (1 cent). The model is a simplistic feed-forward generic Hebbian-learning generic neural network trained with multiple-harmonic complex sounds from the full auditory sound spectrum of 10 octaves. We use the synaptic weights between the neural correlates of each two-tone from the said spectrum to measure the model's preference to their inter-tonal interval (12,000² intervals), considering familiarity as a consonance predictor. We analyze all the 12,000 intervals of a selected tone (the tonic), and the results reveal three distinct yet related fea

SUBMITTER: Pankovski T 

PROVIDER: S-EPMC9132910 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature