## 4 Experiments
### Experimental Setup  
To evaluate the four groups (baseline, timbre all, timbre selected, and combination of baseline and timbre selected) of acoustic features, we performed classification using the SVM and the LSTM-RNN classifiers. We divided the 4 categorical emotions into positive (happiness and neutral) and negative (anger and sadness) emotions, the same as in [5], to perform the binary valence classification. We also carried out classification for categorical emotions. SVM classification was carried out in MATLAB using machine learning and a deep learning toolbox. The kernel function was set to a radial base function (RBF) (Gaussian), gamma was set to automatic, and the c parameter was set to 1. Before feeding the data into the classifier, the z-score normalization was applied to each acoustic feature. To obtain more stable results, we performed a five-fold cross-validation (2 speakers in each fold) in our experiments. The LSTM-RNN classifier was built in a python programming language using the Keras [56] library. It consisted of 2 hidden layers with 256 nodes in each layer. For the activation function, relu was chosen with a 0.6 regularization parameter. The optimization function was Adam, and the loss function was a categorical cross entropy.  
Firstly, classification was carried out for the baseline acoustic features. The average classification rates in all experiments are given in Table 4 for the EMO-DB and in Table 5 for the IEMOCAP. All results were obtained by testing each file and applying the majority win method. For instance, if the speech signal with the anger emotion consisted of 190 frames and the classifier predicted 100 frames as an anger emotion, we considered this file to be an anger emotion. We performed five-fold cross-validation  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Groups** & **Acoustic Feature Name** & **Dimension** \\ \hline \multirow{4}{*}{Baseline} & MFCC & 16 \\  & Delta MFCC & 16 \\  & Energy & 1 \\  & Delta Energy & 1 \\ \hline \multirow{6}{*}{Timbre all} & Spectral Centroid & 1 \\  & Spectral Spread & 1 \\  & Spectral Skewness & 1 \\  & Spectral Kurtosis & 1 \\  & Spectral Slope & 1 \\  & Spectral Decrease & 1 \\  & Spectral Roll-off & 1 \\  & Spectral Flux & 1 \\  & Spectral Energy & 1 \\  & Spectral Flatness & 1 \\  & Spectral Crest & 1 \\  & Harmonic Energy & 1 \\  & Noise Energy & 1 \\  & Noiseness & 1 \\  & F0 & 1 \\  & Inflammatory & 1 \\  & Tristimulus & 3 \\  & Harmonic spectral deviation & 1 \\  & Odd to even harmonic ratio & 1 \\ \hline \multirow{6}{*}{Timbre selected} & Spectral Centroid & 1 \\  & Spectral Spread & 1 \\  & Spectral Flux & 1 \\  & Spectral Energy & 1 \\  & Spectral Crest & 1 \\  & Noiseness & 1 \\  & F0 & 1 \\  & Inharmonicity & 1 \\  & Tristimulus & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detailed information about acoustic features and their dimension.  
(each fold consists of 2 speakers) to get more stable results. Each time 4 folds were used for training and the remaining one was used for testing. The accuracy was taken from each fold, and the final accuracy was calculated by adding the accuracies for each fold and dividing by 5.  
The second experiment was performed using the timbre all acoustic feature set in order to determine the efficiency of the feature set. After that, an SFS algorithm was applied to the timbre all acoustic feature set to find the best feature subset. The third experiment was carried out with the timbre selected acoustic feature set. Finally, the last experiment was performed using a combination of the timbre selected and baseline acoustic feature sets in order to validate whether these acoustic feature sets complemented each other or overlapped.