## 4 Speech Emotion Recognition
### Feature Selection  
Since speech acoustic features are important for emotion recognition, it is essential to select and analyze appropriate features. [PERSON] (2010) found that harmonic structures expressed positive and peaceful emotions, such as happy, cheerful, comfortable, and elegant, whereas dissonant intervals were closely related to negative and sad emotions, such as agitated, tense, angry, etc. Harmonic and dissonant speech intervals are closely related to the speech spectrum harmonic structure [56]. Table 1 shows harmonic features used to quantitatively analyze the harmonic structure. We extract the harmonic features using the features extraction tool Essentia which is open-source library and tools for audio and music analysis [57]. The harmonic energy feature presents the total sum of frequency peaks in a harmonic structure, when frequencies and magnitudes of harmonic peaks were given. Noise refers to the part besides harmonic structure in a spectrum, and the noise energy presents the subtraction of harmonic energy from the entire spectrum energy. The feature of Noiseness is that it represents the ratio of noise in a spectrum and it refers to the value of noise energy divided by the entire sum of spectrum. F0 feature means fundamental frequency of speech. Inharmonicity feature shows the degree of how much a spectrum does not form harmonic structure, and it is calculated as weighted sum of spectrum values which are the closest to multiples of fundamental frequency. Tristimulus feature is divided into 3 aspects; the first is to calculate relative weighted value, the second aspect is to calculate second, third and fourth harmonic's relative weighted value, and the third and the last is to calculate all the rest harmonics' relative weighted value. The detailed description of the harmonic features can be found in [57]. Table 2 shows the test result that distinguishes between Negative Valence and Positive Valence in the same Arousal. We verified distinction in terms of accuracy as to the combination of features by utilizing Medium Gaussian support vector machines. Negative and positive emotions with high arousal were anger and happiness, respectively, and it yielded 65.5% in accuracy when we classified the two emotions by combining features of Noise energy, Noiseness, Inharmonicity, Tristimulus among harmonic features. Negative and positive emotions with low arousal were sadness and neutrality, respectively, and it yielded 76% in accuracy when we classified the two emotions by utilizing features of Noiseness, F0, Inharmonicity, Tristimulus among harmonic features. Through this result, it is clarified that Negative Valence and Positive Valence can be distinguished in the same Arousal when characteristics \begin{table}
\begin{tabular}{c c} \hline \hline
**No** & **Features** \\ \hline
1 & Harmonic energy \\
2 & Noise energy \\
3 & Noiseness \\
4 & F0 \\
5 & Inharmonicity \\
6 & Tristimulus \\
7 & Harmonic spectral deviation \\
8 & Odd to even harmonic ratio \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of sentences recorded for each emotion. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Features** & & **Harmonic Feature Set** & **Accuracy (\%)** \\ \hline High Arousal & Happiness & Anger & Noise energy, Noiseness, Inharmonicity, Tristimulus & 65.5\% \\ Low Arousal & Neutrality & Sadness & Noiseness, F0, Inharmonicity, Tristimulus & 76\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of sentences recorded for each emotion. of Inharmonicity, Tristimuls, Harmonic energy, Noise energy, Noiseness are utilized in combination. Features used for speech emotion recognition were also analyzed, including MFCC, LPC, Zero-Crossing Rate, Signal Energy, FFT Spectrum (magnitude), Mel Spectrum, Cepstral features, Pitch, Harmonic to Noise Ratio, Spectral features, Chroma and others, which have been widely used in previous studies. Through each feature's individual classification, we carried out an experiment to investigate features specialized in emotion recognition.