Subject: IDyOM v1.4 From: Marcus Pearce <marcus.pearce@xxxxxxxx> Date: Fri, 6 May 2016 12:40:28 +0100 List-Archive:<http://lists.mcgill.ca/scripts/wa.exe?LIST=AUDITORY>--------------010701010702030803000506 Content-Type: text/plain; charset="windows-1252"; format=flowed Content-Transfer-Encoding: 7bit Dear All (with apologies for cross-posting), I'm pleased to announce v1.4 of IDyOM (Information Dynamics of Music) - a system for constructing multiple-viewpoint, variable-order Markov models for predictive statistical modelling of auditory sequences. The system generates a conditional probability distribution representing the estimated likelihood of each event in a sequence, given the preceding context; it computes Shannon entropy as a measure of uncertainty about the next event and information content as a measure of the unexpectedness of the event that actually follows. Updates in this release include new functions for database management and modelling melodic segmentation (see Pearce et al., 2010, /Perception/, 39, 1367-1391 <http://webprojects.eecs.qmul.ac.uk/marcusp/papers/PearceEtAlPerception2010.pdf> ), several optimisations for runtime speed, and many other updates and enhancements (see the README file for more). Thanks also to Emily Morgan for useful feedback on the documentation which now contains updated installation notes. The software and documentation are available at: https://code.soundsoftware.ac.uk/projects/idyom-project Marcus -- Senior Lecturer in Sound and Music Processing Queen Mary, University of London Mile End Road, London E1 4NS, UK Tel: +44 (0)20 7882 6207 Web:http://webprojects.eecs.qmul.ac.uk/marcusp Lab:http://music-cognition.eecs.qmul.ac.uk/ --------------010701010702030803000506 Content-Type: text/html; charset="windows-1252" Content-Transfer-Encoding: 7bit <html> <head> <meta http-equiv="content-type" content="text/html; charset=windows-1252"> </head> <body bgcolor="#FFFFFF" text="#000000"> Dear All (with apologies for cross-posting),<br> <div class="moz-forward-container"> <br> I'm pleased to announce v1.4 of IDyOM (Information Dynamics of Music) - a system for constructing multiple-viewpoint, variable-order Markov models for predictive statistical modelling of auditory sequences. The system generates a conditional probability distribution representing the estimated likelihood of each event in a sequence, given the preceding context; it computes Shannon entropy as a measure of uncertainty about the next event and information content as a measure of the unexpectedness of the event that actually follows.<br> <br> Updates in this release include new functions for database management and modelling melodic segmentation (see <a moz-do-not-send="true" href="http://webprojects.eecs.qmul.ac.uk/marcusp/papers/PearceEtAlPerception2010.pdf">Pearce et al., 2010, <i>Perception</i>, 39, 1367-1391</a> ), several optimisations for runtime speed, and many other updates and enhancements (see the README file for more). Thanks also to Emily Morgan for useful feedback on the documentation which now contains updated installation notes.<br> <br> The software and documentation are available at:<br> <br> <a moz-do-not-send="true" class="moz-txt-link-freetext" href="https://code.soundsoftware.ac.uk/projects/idyom-project">https://code.soundsoftware.ac.uk/projects/idyom-project</a><br> <br> Marcus <pre class="moz-signature" cols="72">-- Senior Lecturer in Sound and Music Processing Queen Mary, University of London Mile End Road, London E1 4NS, UK Tel: +44 (0)20 7882 6207 Web: <a moz-do-not-send="true" class="moz-txt-link-freetext" href="http://webprojects.eecs.qmul.ac.uk/marcusp">http://webprojects.eecs.qmul.ac.uk/marcusp</a> Lab: <a moz-do-not-send="true" class="moz-txt-link-freetext" href="http://music-cognition.eecs.qmul.ac.uk/">http://music-cognition.eecs.qmul.ac.uk/</a></pre> <br> </div> <br> </body> </html> --------------010701010702030803000506--