---------- Forwarded message ---------
From: Marcus Pearce <0001ca105d8b2838-dmarc-request@jiscmail.ac.uk>
Date: Mon, 28 Sept 2026 at 20:50
Subject: IDyOM v1.8
To: <MUSIC-AND-SCIENCE@jiscmail.ac.uk>
From: Marcus Pearce <0001ca105d8b2838-dmarc-request@jiscmail.ac.uk>
Date: Mon, 28 Sept 2026 at 20:50
Subject: IDyOM v1.8
To: <MUSIC-AND-SCIENCE@jiscmail.ac.uk>
**This list is managed by Professor Evangelos Himonides, University College London (UCL). Before 2015, this list was managed by the Institute of Musical Research (IMR).** MESSAGE FOLLOWS:
Dear all,
I'm pleased to announce v1.8 of IDyOM (Information Dynamics of Music). Documentation and downloads are available at:
https://mtpearce.github.io/idyom/
This release contains updates to harmony modelling alongside general bug fixes. There is also improved documentation for modelling harmony and similarity as well as tools for music generation and key finding. See the README for full details of new features in this release.
IDyOM is a computational framework for constructing multiple-viewpoint, variable-order Markov models for predictive modelling of probabilistic structure in symbolic, sequential auditory domains such as music. IDyOM acquires information about a domain through statistical learning and generates conditional probability distributions representing the estimated likelihood of each event in a sequence, plus associated information-theoretic measures, given the preceding context and prior short- and long-term training of the model. IDyOM has been used to simulate musical expectations, perceptual similarity, auditory memory, complexity perception and experience of pleasure.
Marcus
I'm pleased to announce v1.8 of IDyOM (Information Dynamics of Music). Documentation and downloads are available at:
https://mtpearce.github.io/idyom/
This release contains updates to harmony modelling alongside general bug fixes. There is also improved documentation for modelling harmony and similarity as well as tools for music generation and key finding. See the README for full details of new features in this release.
IDyOM is a computational framework for constructing multiple-viewpoint, variable-order Markov models for predictive modelling of probabilistic structure in symbolic, sequential auditory domains such as music. IDyOM acquires information about a domain through statistical learning and generates conditional probability distributions representing the estimated likelihood of each event in a sequence, plus associated information-theoretic measures, given the preceding context and prior short- and long-term training of the model. IDyOM has been used to simulate musical expectations, perceptual similarity, auditory memory, complexity perception and experience of pleasure.
Marcus
-- Dr Marcus Pearce School of Electronic Engineering and Computer Science Queen Mary University of London https://www.marcus-pearce.com New book: Learning to Listen, Listening to Learn https://academic.oup.com/book/60588
