Mercurial > hg > syncopation-dataset

--- a/SMC2015latex/section/background.tex	Mon Apr 27 10:00:26 2015 +0100
+++ b/SMC2015latex/section/background.tex	Mon Apr 27 11:55:05 2015 +0100
@@ -1,6 +1,8 @@
 \section{Background}
+\label{sec:background}

 \subsection{Rhythm representation}
+\label{sec:background:rhythm}
 In this section, we introduce some key concepts to assist readers in understanding the mechanisms of
 each syncopation model. Please refer to \cite{Song14thesis} for the detailed explanation all relevant rhythmic concepts in music theory and their mathematical notations.

@@ -11,6 +13,7 @@
 \end{figure}

 \subsubsection{Time-span}
+\label{sec:background:rhythm:timespan}
 The term \emph{time-span} has been defined as the period between two points in time, including all time points in between \cite{Lerdahl_Jackendoff83GTTM}. To represent a given rhythm, we must specify the time-span within which it occurs by defining a reference time origin $\timeorigin$ and end time $\timeend$, the total duration $\timespan$ of which is $\timespan = \timeend-\timeorigin$ (Figure~\ref{fig:general}.

 The basic time unit is in \emph{ticks} as opposed to seconds, therefore we set the parameter Ticks Per Quarter-note (TPQ) to describe the time-span of a length of rhythm. The minimum TPQ is determined by the rhythm-pattern so that all the events can be represented. As demonstrated in Figure~\ref{fig:clave}, the \emph{Son} clave rhythm pattern could be represented both at 8 and 4 ticks per quarter-note but the minimum representable resolution would be 4.
@@ -22,6 +25,7 @@
 \end{figure}

 \subsubsection{Note and rhythm}
+\label{sec:background:rhythm:note}
 A single, \emph{note} event $\note$ occurring in this time-span may be described by the tuple $(\starttime, \durationtime, \velocity)$ as shown in Figure~\ref{fig:general}, where $\starttime$ represents start or \emph{onset} time relative to $\timeorigin$, $\durationtime$ represents note duration in the same units and $\velocity$ represents the note \emph{velocity} (i.e. the dynamic; how loud or accented the event is relative to others), where $\velocity > 0$.

 This allows us to represent an arbitrary rhythm as a note sequence $\sequence$, ordered in time
@@ -53,40 +57,92 @@
 \end{equation}
 , which is different from the original note sequence in Equation~\ref{eq:note_sequence}.

-\subsubsection{Metrical levels and Time-signature}
+\subsubsection{Metrical structure and time-signature}
+\label{sec:background:rhythm:meter}
 \begin{figure}
 \centerline{\epsfig{figure=figs/ch_model/meter_hierarchy7.pdf, width=0.85\columnwidth}}
-\shortCap{Metrical hierarchies for different time-signatures.}{(a) A simple-duple hierarchy dividing the bar into two groups of two (as with a 4/4 time-signature). (b) A simple-triple hierarchy dividing a bar into three beats, each of which is subdivided by two (e.g. 3/4 time-signature). (c) A compound-duple hierarchy dividing a bar into two beats, each of which is subdivided by three (e.g. 6/8 time-signature). Reading the weights from left to right in any level $\metriclevel$ gives the elements in sequence $\metricvector_\metriclevel$ (see Equations~\ref{eq:def_metricvector2} and \ref{eq:def_metricvector}).} %showing strong and weak beat positions along with the metrical `weights' associated with each node in the tree:
+\shortCap{Metrical hierarchies for different time-signatures.}{(a) A simple-duple hierarchy dividing the bar into two groups of two (as with a 4/4 time-signature). (b) A compound-duple hierarchy dividing a bar into two beats, each of which is subdivided by three (e.g. 6/8 time-signature). Reading the weights from left to right in any level $\metriclevel$ gives the elements in sequence $\metricvector_\metriclevel$}
 \label{fig:meter-hierarchy}
 \end{figure}

 Isochronous-meter is formed with a multi-level hierarchical metrical structure~\cite{Lerdahl_Jackendoff83GTTM, London04Meter}. As shown in Figure~\ref{fig:meter-hierarchy}, under a certain metrical hierarchy, a bar is divided by a subdivision factor $\subdivision$ at each metrical level with index $\metriclevel$ where $\metriclevel \in [0, \levelmax]$. The list of subdivision factors is referred as a \emph{subdivision sequence}.

-Events at different metrical positions vary in perceptual salience or \emph{metrical weight}~\cite{Palmer_Krumhansl90}. These weights may be represented as a \emph{weight sequence} $\metricweightset = \langle \metricweight_0, \metricweight_1, ... \metricweight_{\levelmax}\rangle$.  The choice of values for the weights in $\metricweightset$ can vary between different models but the assignment of weights to nodes is common to all as in ~\cite{Lerdahl_Jackendoff83GTTM}.
+Events at different metrical positions vary in perceptual salience or \emph{metrical weight}~\cite{Palmer_Krumhansl90}. These weights may be represented as a \emph{weight sequence} $\metricweightset = \langle \metricweight_0, \metricweight_1, ... \metricweight_{\levelmax}\rangle$. The prevailing hypothesis for the assignment of weights in the metrical hierarchy is that a time point that exists in both the current metrical level and the level above is said to have a \emph{strong} weight compared gto time points that are not also present in the level above~\cite{Lerdahl_Jackendoff83GTTM}. The choice of values for the weights in $\metricweightset$ can vary between different models but the assignment of weights to nodes is common to all as in ~\cite{Lerdahl_Jackendoff83GTTM}.

 \subsection{Syncopation models}
+\label{sec:background:models}
+In this section we give a brief review of each implemented syncopation model, including their general hypothesis, mechanism and scope of capabilities.

-\subsection{Longuet-Higgins and Lee 1984 (\lhl)}
+\subsubsection{Longuet-Higgins and Lee 1984 (\lhl)}
+\label{sec:background:models:lhl}

+Longuet-Higgins and Lee's \cite{LHL84} decomposes rhythm pattern into a tree structure from Section~\ref{sec:background:rhythm:meter} with metrical weights as $\metricweight_\metriclevel = -\metriclevel$ for all $\metricweight_\metriclevel \in \metricweightset$ i.e. $\metricweightset = \langle 0,-1,-2, ... \rangle$.
+The hypothesis of this model is that a syncopation occurs when a rest ($\RestNode$) in one metrical position follows a note ($\NoteNode$) in a weaker position.  Where such a note-rest pair occurs, the difference in their metrical weights is taken as a local syncopation score. Summing  the local scores produces the syncopation prediction for the whole rhythm sequence.

-\subsection{Pressing 1997 (\pressing)}
+\subsubsection{Pressing 1997 (\pressing)}
+\label{sec:background:models:prs}
+Pressing's cognitive complexity model~\cite{Pressing97,Pressing93} specifies six prototype binary sequences and ranks them in terms of \emph{cognitive cost}. For example, the \emph{filled} prototype that has a note in ever position of the sequence (e.g. $\langle 0,1,1,1,0,1,1,1 \rangle$) cost less than the \emph{syncopated} prototype that has a 0 in the first, strongest metrical position (e.g. $\langle 0,1,1,1,0,1,1,1 \rangle$)(refer~\cite{Song14thesis} for details). The model analyses the cost for the whole rhythm-pattern and its sub-sequences at each metrical level determined by $\subdivision_\metriclevel$. The final output will be a weighted sum of the costs by the number of sub-sequences in each level.

-\subsection{Toussaint 2002 `Metric Complexity'  (\metrical)}
+\subsubsection{Toussaint 2002 `Metric Complexity' (\metrical)}
+\label{sec:background:models:tmc}
+Toussaint's \emph{metric complexity} measure \cite{Toussaint02Metrical} defines the metrical weights as $\metricweight_\metriclevel = \metriclevel_{\textrm{max}} - \metriclevel +1$, thus stronger metrical position is associated with higher weight and the weakest position will be $\metricweight_{\metriclevel_{\textrm{max}}}=1$.

-\subsection{Sioros and Guedes 2011 (\sioros)}
+\subsubsection{Sioros and Guedes 2011 (\sioros)}
+\label{sec:background:models:sg}
+Sioros and Guedes~\cite{Sioros11,Sioros12} has three main hypotheses: First, accenting of notes affects perceived syncopation and should be included in the model (the only model in this study to do so). Second, humans try to minimise the syncopation of a particular note relative to its neighbours in each level of the metrical hierarchy. Third, syncopations at the beat level are more salient than those that occur in higher or lower metrical levels so the outcome should be scaled to reflect this~\cite{Sioros13}.

-\subsection{Keith 1991 (\keith)}
+\subsubsection{Keith 1991 (\keith)}
+\label{sec:background:models:kth}

-\subsection{Toussaint 2005 `Off-Beatness' (\offbeat)}

-\subsection{G\'omez 2005 `Weighted Note-to-Beat Distance' (WNBD)}
+\subsubsection{Toussaint 2005 `Off-Beatness' (\offbeat)}
+\label{sec:background:models:tob}


+\subsubsection{G\'omez 2005 `Weighted Note-to-Beat Distance' (WNBD)}
+\label{sec:background:models:wnbd}


+\subsubsection{Capabilities of models}
+\label{sec:background:models:capabilities}

+\begin{table}
+\centering
+\caption{Comparisons of the properties of syncopation models.}
+\label{ta:capabilites}
+\begin{tabular}{c | c c c c c c c}
+\hline
+Property & \lhl & \pressing & \metrical & \sioros & \keith & \offbeat & \wnbd \\
+\hline
+Onset & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark \\
+Duration & & & & & \checkmark & & \checkmark \\
+Dynamics & & & & \checkmark & & & \\
+Mono & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark \\
+Poly & & & & & \checkmark & \checkmark & \checkmark \\
+Duple & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark \\
+Triple & \checkmark & \checkmark & \checkmark & \checkmark & & \checkmark & \checkmark \\
+\hline
+%\hline
+%Model & Basis & Onset & Dynamics & Melody & Mono & Poly & Duple & Triple \\
+%\hline
+%\lhl & H & \checkmark & & & \checkmark & & \checkmark & \checkmark\\
+%\keith & C & \checkmark & & & \checkmark & \checkmark & \checkmark & \\
+%\pressing & H,C & \checkmark & & & \checkmark & & \checkmark & \checkmark\\
+%\metrical & H & \checkmark & & & \checkmark & & \checkmark & \checkmark\\
+%\offbeat & O & \checkmark & & & \checkmark & \checkmark & \checkmark & \checkmark\\
+%\wnbd & O & \checkmark & & & \checkmark & \checkmark & \checkmark & \checkmark\\
+%\sioros & H & \checkmark & \checkmark & & \checkmark & & \checkmark & \checkmark\\
+%\ksa & A & \checkmark & \checkmark & \checkmark & \checkmark & & \checkmark & \\
+%\hline
+\end{tabular}
+\end{table}

+To summarise the seven syncopation models, we compare their capabilities in terms of musical features they can capture in Table~\ref{ta:capabilites}. All the models use temporal features (i.e. onset time point and/or note duration) in the modelling. The SG model also process dynamic information of musical events (i.e. note velocity). We use the term \emph{monorhythm} to refer to any rhythm-pattern that is not polyrhythmic. All the models can measure syncopation of monorhythms, but only KTH, TOB and WNBD models can deal with polyrhythms. Finally, all the models can deal with rhythms (notated) in duple meter, but all models except KTH can cope with rhythms in a triple meter.


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--- a/SMC2015latex/section/introduction.tex	Mon Apr 27 10:00:26 2015 +0100
+++ b/SMC2015latex/section/introduction.tex	Mon Apr 27 11:55:05 2015 +0100
@@ -1,8 +1,9 @@
 \section{Introduction}
+\label{sec:introduction}

 Syncopation is a fundamental feature of rhythm in music and a crucial aspect of musical character in many styles and cultures. Having comprehensive models to capture syncopation perception allows us to better understand the broad aspects of music perception. Over the last thirty years, several modelling approaches for syncopation have been developed and heavily used in studies in multiple disciplines~\cite{Fitch_Rosenfeld07, Smith_Honing07, Keller_Schubert11, Madison13, Witek14}. To date, formal investigations on the links between syncopation and  music perception subjects such as meter induction, emotion and groove, have largely relied on quantitative measures of syncopation [cites?]. However, until now there has not been a comprehensive reference implementation of the different algorithms available to facilitate quantifying syncopation.

-In~\cite{Song14thesis}, Song provides a consolidated mathematical framework and in-depth review of seven widely used syncopation models including: Longuet-Higgins and Lee’s model (LHL)~\cite{LHL84}, Pressing’s model (PRS)~\cite{Pressing97,Pressing93}, Toussaint’s Metric Complexity model (TMC)~\cite{Toussaint02Metrical}, Sioros and Guedes’s model (SG)~\cite{Sioros11,Sioros12}, Keith’s model (KTH)~\cite{Keith91}, Toussaint's off-beatness measure (TOB)~\cite{Toussaint05Offbeatness} and G\’omez et al.’s Weighted Note-to- Beat Distance (WNBD)~\cite{Gomez05}.
+In~\cite{Song14thesis}, Song provides a consolidated mathematical framework and in-depth review of seven widely used syncopation models including: Longuet-Higgins and Lee’s model (LHL)~\cite{LHL84}, Pressing’s model (PRS)~\cite{Pressing97,Pressing93}, Toussaint’s Metric Complexity model (TMC)~\cite{Toussaint02Metrical}, Sioros and Guedes’s model (SG)~\cite{Sioros11,Sioros12}, Keith’s model (KTH)~\cite{Keith91}, Toussaint's off-beatness measure (TOB)~\cite{Toussaint05Offbeatness} and G\’omez et al.'s Weighted Note-to- Beat Distance (WNBD)~\cite{Gomez05}.
 Based on this mathematical framework, the SynPy toolkit provides implementations of these syncopation models in the Python programming language.

 XXXXX Key features XXXXX. For ease of input, the SynPy toolkit is able to process standard MIDI files or text annotations of rhythm patterns in an intuitive, simple syntax. It is able to process multiple bars of music, reporting syncopation values bar by bar as well as various descriptive statistics across a whole piece. This toolkit also defines a common interface for syncopation models, which provides a simple plugin architecture for future extensibility.