## 4 Evaluation  
We evaluated the proposed model for the discovery of repeated themes and sections task in symbolic monophonic music pieces. Since we are searching for patterns within a given piece (and not across the entire corpus) the model was built independently for each piece and inferred on the same piece. All model parameters were kept constant during all evaluations and were not tuned to each specific case. The parameters were set to the values defined in Table 1. The \(\tau_{W}\) parameter limiting the time span of activations was set to \(\tau_{W}=2^{n+2}\) events. The values and short descriptions of parameters are also listed in Table 1. The values for the \(\tau_{H}\) and \(\tau_{I}\) parameters are based on the stable performance achieved in the range around 0.5 for (see the Sensitivity to parameter values subsection. The \(\tau_{R}\) and \(\tau_{M}\) values were set to the majority thresholds of 50% and were not tuned. The \(\tau_{L}\) parameter value was retained from the original spectral CHM where it was evaluated empirically.  
Table 2 shows the performance of SymCHM on the MIREX 2015 discovery of repeated themes and sections task. To compare SymCHM to SymCHMMerge, the Table 2 also includes the results of their evaluation on the publicly available JKU Patterns Development Dataset (PDD) [44]. Detailed results of SymCHMMerge on this dataset are shown in Table 3.  
The JKU PDD dataset (the dataset is publicly available on this link: [[https://dl.dropbox.com/u/11997856/JKU/JKUPDD-Aug2013.zip](https://dl.dropbox.com/u/11997856/JKU/JKUPDD-Aug2013.zip)]([https://dl.dropbox.com/u/11997856/JKU/JKUPDD-Aug2013.zip](https://dl.dropbox.com/u/11997856/JKU/JKUPDD-Aug2013.zip))) consists of five pieces:  
* [PERSON]'s Prelude and Fugue in A minor (BWV(Bach-Werke-Verzeichnis) 889): 731 note events, 3 patterns, 21 pattern occurrences,
* [PERSON]'s Piano Sonata in F minor (Opus 2, No. 1), third movement: 638 note events, 7 patterns, 22 pattern occurrences,
* [PERSON]'s Mazurka in B flat minor (Opus 24, No. 4): 747 note events, 4 patterns, 94 pattern occurrences,
* [PERSON] \"The Silver Swan\": 347 note events, 8 patterns, 33 pattern occurrences,
* [PERSON]'s Piano Sonata in E flat major, K. 282-2 nd movement: 923 note events, 9 patterns, 38 pattern occurrences.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \multirow{2}{*}{\(\tau_{H}\)} & Hallucination parameter retaining the activation of a part in an & \\  & incomplete presence of the events in the input signal & 0.5 \\ \hline \multirow{2}{*}{\(\tau_{I}\)} & Inhibition parameter reducing the number of competing activations & 0.4 \\ \hline \multirow{2}{*}{\(\tau_{R}\)} & Redundancy parameter determining the the necessary amount of & \\  & overlapping pattern occurrences in order for the occurrences to be merged & \\ \hline \multirow{2}{*}{\(\tau_{M}\)} & Merging parameter determining the amount of redundant pattern & \\  & occurrences needed for two patterns to be merged into one & \\ \hline \multirow{2}{*}{\(\tau_{L}\)} & Learning threshold for added coverage which needs to be exceeded & \\  & in order for a candidate composition to be retained while learning the model & \\ \hline \multirow{2}{*}{\(\tau_{W}\)} & Window limiting the time span of activations, defined per layer \(\mathcal{L}_{n}\) & \(2^{n+2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model\({}^{\prime}\)s parameter settings for the experiment.