# HG changeset patch # User Henrik Ekeus # Date 1328578616 0 # Node ID dd51fad87cd9c70ac9014f6e3b27cc562265409b # Parent ce5631a0ad74df1278ac91f87393ad4c77efea35 Fixed further work. Tidy. diff -r ce5631a0ad74 -r dd51fad87cd9 nime2012/mtriange.pdf Binary file nime2012/mtriange.pdf has changed diff -r ce5631a0ad74 -r dd51fad87cd9 nime2012/mtriange.tex --- a/nime2012/mtriange.tex Mon Feb 06 17:09:14 2012 +0000 +++ b/nime2012/mtriange.tex Tue Feb 07 01:36:56 2012 +0000 @@ -86,7 +86,9 @@ \subsubsection{Predictive Information Rate} [todo - a more formal description] -Predictive information rate tell us the average reduction in uncertainty upon perceiving a symbol; a system with high predictive information rate means that each symbol tells you more about the next one. If we imagine a purely periodic sequence, each symbol tells you nothing about the next one that we didn't already know as we already know how the pattern is going. Similarly with a seemingly uncorrelated sequence, seeing the next symbol does not tell us anymore because they are completely independent anyway; there is no pattern. There is a subset of transition matrixes that have high predictive information rate, and it is neither the periodic ones, nor the completely un-corellated ones. Rather they tend to yield output that have certain characteristic patterns, however a listener can't necessarily know when they occur. However a certain sequence of symbols might tell us about which one of the characteristics patterns will show up next. Each symbols tell a us little bit about the future but nothing about the infinite future, we only learn about that as time goes on; there is continual building of prediction. +Predictive information rate tell us the average reduction in uncertainty upon perceiving a symbol; a system with high predictive information rate means that each symbol tells you more about the next one. + +If we imagine a purely periodic sequence, each symbol tells you nothing about the next one that we didn't already know as we already know how the pattern is going. Similarly with a seemingly uncorrelated sequence, seeing the next symbol does not tell us anymore because they are completely independent anyway; there is no pattern. There is a subset of transition matrixes that have high predictive information rate, and it is neither the periodic ones, nor the completely un-corellated ones. Rather they tend to yield output that have certain characteristic patterns, however a listener can't necessarily know when they occur. However a certain sequence of symbols might tell us about which one of the characteristics patterns will show up next. Each symbols tell a us little bit about the future but nothing about the infinite future, we only learn about that as time goes on; there is continual building of prediction. @@ -187,39 +189,36 @@ The Melody Triangle can also be explored with a standard keyboard and mouse interface. A triangle is drawn on the screen, screen space thus mapped to the statistical space of the Melody Triangle. A number of round tokens, each representing a melody can be dragged in and around the triangle. When a token is dragged into the triangle, the system will start generating the sequence of notes with statistical properties that correspond to its position in the triangle. -Additionally there are a number of keyboard controls. These include controls for changing the overall tempo, for enabling and disabling individual voices, changing registers, going to off-beats and changing the speed of individual voices. The system gives some feedback by way of colour changes to indicate when a token has locked on to a new melody, and contains a buffer zone for allowing tokens to be pushed right to the edges of the triangle without falling out. +Additionally there are a number of keyboard controls. These include controls for changing the overall tempo, for enabling and disabling individual voices, changing registers, going to off-beats and changing the speed of individual voices. The system gives visual feedback to indicate when a token has locked on to a new melody, and contains a buffer zone for allowing tokens to be pushed right to the edges of the triangle without falling out. -In this mode, the Melody Triangle can be used as a kind of composition assistant. Unlike other composition tools, . There is little - -[TODO: discussion on its use as a composition assistant.. some comments on the aesthetics of the output (why it all sounds like minimalism.) why intreresting] - +In this mode, the Melody Triangle can be used as a kind of composition assistant for the generation of interesting musical textures and melodies. However unlike other computer aided composition tools or programming environments, here the composer engages with the musical process on a very high and abstract level; notions of predictability, expectation and surprise the control parameters. \section{Musical Preference and Information Dynamics Study} -We carried out a preliminary study that sought to determine any correlation between aesthetic preference and the information theoretical measures of the Melody Triangle. In this study participants were asked to use the screen based interface of the Melody Triangle. It was simplified so that all they could do was move tokens around. To help discount visual biases, the axes of the triangle would be randomly rearranged for each participant. +We carried out a preliminary study that sought to identify any correlation between aesthetic preference and the information theoretical measures of the Melody Triangle. In this study participants were asked to use the screen based interface but it was simplified so that all they could do was move tokens around. To help discount visual biases, the axes of the triangle would be randomly rearranged for each participant. The study was divided in to two parts, the first investigated musical preference with respect to single melodies at different tempos. In the second part of the study, a background melody is playing and the participants are asked to find a second melody that 'works well' with the background melody. For each participant this was done four times, each with a different background melody from four different areas of the Melody Triangle. After the study the participants were surveyed with the Goldsmiths Musical Sophistication Index\cite{Mullensiefen:2011ts} to elicit their prior musical experience. \subsection{Results} -X participants took part in the study (mean median age). (Prior musical experience? ) +[todo] \subsection{Observation/Discussion} - +[todo] \section{Further Work} -In using first-order Markov chains the patterns generated don't have any long term structure or form. The Melody Triangle only works in the musical `present'; its melodies don't `go anywhere' in the long term, and as such is better suited to creating textures and patterns as oppose to composing over-arching musical structures. +In using first-order Markov chains the patterns generated don't have any long term structure or form and as such the melodies generated don't seem to `go anywhere' in the long term. The Melody Triangle and is better suited to creating textures and patterns as oppose to composing over-arching musical structures. We are currently investigating how higher-order Markov models can be mapped to information theoretic measures and if the Melody Triangle could be adapted to those models. This would generate a higher level patterns and provide more long-term structures. As it stands, the streams of symbols generated are only mapped to note values. However they could just as well be applied to any other musical property, such as intervals, chords, dynamics, timbres, structures and key changes. The possibilities for the Melody Triangle to be compositional guide in these other domains remains to be investigated. -The Melody Triangle in its current form however forms an ideal tool for investigations into musical preference and their relationship to the information dynamics models, and as such more detailed studies under wider experimental conditions and with more participants can be carried out. +The Melody Triangle in its current form however forms an ideal tool for investigations into musical preference and their relationship to the information dynamics models, and as such more detailed studies under wider experimental conditions and with more participants will be carried out. \section{acknowledgments} -This work is supported by EPSRC Doctoral Training Centre EP/G03723X/1 (HE), GR/S82213/01 and EP/E045235/1(SA), an EPSRC Leadership Fellowship, EP/G007144/1 (MDP) and EPSRC IDyOM2 EP/H013059/1. Thanks to Louie McCallum and Davie Smith from QMUL for Kinect programming support. +This work is supported by EPSRC Doctoral Training Centre EP/G03723X/1 (HE), GR/S82213/01 and EP/E045235/1(SA), an EPSRC Leadership Fellowship, EP/G007144/1 (MDP) and EPSRC IDyOM2 EP/H013059/1. Thanks to Louie McCallum and Davie Smith from QMUL EECS for Kinect programming support. \bibliographystyle{abbrv} \bibliography{nime} \end{document}