## 4 Experimental Results
### Model-Based Approach Based on Spectral Features and Conventional Machine Learning Algorithms  
Three regression methods were used for predicting the scores indicating the activation and valence levels ranged from \(-1\) and \(1\). The three regression methods used are principal component regression (PCR), partial least squares (PLS) regression, and a feedforward network. The feedforward network uses tan-sigmoid transfer functions in 30 hidden layers and a linear transfer function in the output layer. To evaluate the predictive power of the STC-based features, the baseline features and the STC-based features were first evaluated separately, then the combined set (baseline + STC) was evaluated. The three regression methods were trained and tested using a 10-fold cross-validation technique, wherein the segments of a song are only included in the same fold. The features were extracted at \begin{table}
\begin{tabular}{l l} \hline \hline
**Num.** & **Description** \\ \hline
1 & maximum \\
2 & minimum \\
3 & mean \({}^{*}\) \\
4 & standard deviation \\
5 & kurtosis \({}^{*}\) \\
6 & skewness \\
7\(\sim\)9 & 1 st, 2 nd, \& 3 rd quartiles \({}^{*}\) \\
10 & interquartile range \({}^{*}\) \\
11\(\sim\)12 & 1 st \& 99 th percentiles \({}^{*}\) \\
13 & RMS value \\
14 & slope of linear regression \({}^{*}\) \\
15 & approximation error of linear regression \({}^{*}\) \\ \hline \hline \end{tabular} \({}^{*}\): applied to \(A_{k}\), \(f_{k}\), \(\Delta A_{k}\), \(\Delta f_{k}\), and the peak locations and magnitudes of \(R_{FF}\).  
\end{table} 
Table 1: List of statistical and regression measures applied to low-level descriptors. a phrase level. As described in Section 3.2, the phrase level is 8 s long with 50% overlap. Moreover, the number of principal and predictor components for the PCR and PLS methods were varied from 1 to 150 and from 1 to 20, respectively. For the feedforward method, the number of hidden layers was varied from 1 to 30, but no clear trend was observed. The root mean square errors (RMSE) were calculated for evaluation. For PCR and PLS, the prediction RMSE are shown in Figure 3 with varying the number of components. The STC-based features alone may not outperform the baseline features; however, the inclusion of the STC-based features with the baseline features show an improvement in reducing the prediction errors. Using the PCR method, a trend of decrease in RMSE can be observed as the number of the principal components increases. After a certain point, the reduction rate in RMSE seems to be saturated. We further increased the number of principal components from 150 to 300; however the error reduction rate was not significant. For the arousal dimension, using the combined features with 150 principal components, the RMSE was 0.144, whereas using the baseline features alone, the RMSE was 0.147. Using the PLS method, the lowest errors in the activation dimension were obtained when using 4 components for the combined features, and using 7 components for the baseline features. As shown in Figure 3a, a larger number of components does not provide a better prediction error. The lowest RMSE was 0.144 when the combined features were used, and it was 0.146 when the baseline features were used alone. Using the feedforward method, the combined features provided the lowest RMSE with 9 hidden layers. The lowest RMSE using the feedforward method was 0.165, whereas it was 0.172 when the baseline features were used alone. In human emotion analysis, it is well known that classifying the valence dimension is relatively difficult when only audio modality is used [19; 20; 21]. Similarly, the prediction errors (RMSEs) for the valence dimension is higher than the RMSEs of the activation dimension as shown in Figure 3b. For the valence dimension, using the PCR method, the lowest RMSE was 0.151 using the combined features,  
Figure 3: Root mean square errors (RMSEs) for the arousal dimension using (**a**) principal component regression models and (**b**) partial least square models.