Subject: [AUDITORY] IDyOM v1.7 From: Marcus Pearce <marcus.pearce@xxxxxxxx> Date: Mon, 22 May 2023 16:04:34 +0100--------------QLTW0Rlg5I4Z9IyS6QNreR0D Content-Type: text/plain; charset=UTF-8; format=flowed Content-Transfer-Encoding: 7bit Dear All, I'm pleased to announce v1.7 of *IDyOM (Information Dynamics of Music)*. Downloads and documentation are available from: http://mtpearce.github.io/idyom/ See the README for details of what's new in this release. IDyOM is a system 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. Marcus -- School of Electronic Engineering and Computer Science Queen Mary University of London, London E1 4NS, UK Web:http://www.marcus-pearce.com Lab:http://music-cognition.eecs.qmul.ac.uk --------------QLTW0Rlg5I4Z9IyS6QNreR0D Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: 7bit <html><head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8"> </head> <body> Dear All,<br> <br> I'm pleased to announce v1.7 of <b>IDyOM (Information Dynamics of Music)</b>. Downloads and documentation are available from:<br> <br> <a class="moz-txt-link-freetext" href="http://mtpearce.github.io/idyom/">http://mtpearce.github.io/idyom/</a><br> <br> See the README for details of what's new in this release. <br> <br> IDyOM is a system 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.<br> <br> Marcus<br> <pre class="moz-signature">-- School of Electronic Engineering and Computer Science Queen Mary University of London, London E1 4NS, UK Web: <a class="moz-txt-link-freetext" href="http://www.marcus-pearce.com">http://www.marcus-pearce.com</a> Lab: <a class="moz-txt-link-freetext" href="http://music-cognition.eecs.qmul.ac.uk">http://music-cognition.eecs.qmul.ac.uk</a> </pre> </body> </html> --------------QLTW0Rlg5I4Z9IyS6QNreR0D--