## 4 Analysis of Program Clusters Based on Audio Features
### Feature Extraction  
In total, 25 features in different dimensions including dynamics, rhythm, timbre, and spectrum were extracted for each program excerpt. Many feature extraction tasks were aided by the MIRtoolbox [18]. Table 3 presents the list of the extracted features, and also the ANOVA results evaluating if significant differences in these features exist between different program clusters. These features are often used to characterize a segment of audio [18; 19; 20; 27; 28; 29; 30; 31; 32]. In the dynamic dimension, the root-mean-square (RMS) and peak value were measured to describe the amplitude of the signal. The crest factor, which is the ratio of peak to RMS value, was calculated to evaluate how dynamic the program is. The low energy rate [28] is the percentage of frames showing less-than-average energy, which assesses the temporal distribution of energy. In the rhythmic dimension, tempo describes the periodicities of the signal, and pulse clarity [32] estimates the rhythmic clarity, suggesting how easily listeners can perceive the underlying pulsation of the program. In the timbral dimension, several features (e.g., attack time, attack slope, decay time, decay slope) were calculated based on the temporal energy envelope [19], describing the attack phase and decay phase separately. The duration feature [20] estimates the duration of each successive event, distinguishing percussive sounds from sustained sounds. The zero-crossing rate measures the number of times the signal crosses the zero axis. In the spectral dimension, the energy distribution was determined using a bank of filters. The full band was divided into seven bands: frequencies below 50 Hz, 50 Hz to 100 Hz, 100 Hz to 200 Hz, 200 Hz to 400 Hz, 400 Hz to 800 Hz, 800 Hz to 1600 Hz, and frequencies above 1600 Hz. The proportion of the energy of each filtered output to the total energy was calculated. In Table 3, these features are labeled with the prefix BE, which stands for Band Energy. In addition, some statistical features describing the shape of the spectrum (e.g., skewness, kurtosis, flatness, centroid, spread, roll-off) were also extracted. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Feature** & **F** & **\(\mathbf{p}\)** \\ \hline \multirow{6}{*}{Dynamic} & RMS & 1.028 & 0.324 \\  & Peak & 0.047 & 0.831 \\  & Crest factor & 0.366 & 0.553 \\  & Low energy rate & 0.473 & 0.500 \\  & **Tempo** & 5.940 & 0.025 * \\ Rhythmic & **Pulse clarity** & 9.044 & 0.008 * \\  & Attack time & 0.057 & 0.814 \\  & Attack slope & 1.045 & 0.320 \\  & Decay time & 1.193 & 0.289 \\ Timbral & Decay slope & 0.428 & 0.521 \\  & Duration & 0.007 & 0.936 \\  & Zero-crossing rate & 1.606 & 0.221 \\  & BE \(<\) 50 & 0.037 & 0.849 \\  & BE 50–100 & 0.436 & 0.517 \\  & BE 100–200 & 0.014 & 0.908 \\  & BE 200–400 & 0.004 & 0.952 \\  & **BE 400–800** & 15.710 & 0.001 * \\  & **BE 800–1600** & 5.820 & 0.027 * \\  & **BE \textgreater{} 1600** & 5.606 & 0.029 * \\  & **Skewness** & 6.787 & 0.018 * \\  & **Kurtosis** & 8.174 & 0.010 * \\  & Flatness & 1.123 & 0.303 \\  & Centroid & 2.138 & 0.162 \\  & Spread & 2.134 & 0.161 \\  & Roll-off & 3.236 & 0.089 \\ \hline \hline \end{tabular}  
* Significance level 0.05.  
\end{table} 
Table 3: The list of extracted features and one-way analysis of variance (ANOVA) results. As listed in Table 3, among the 25 features, seven features (highlighted in bold) were found significantly differentiated between the two clusters (where \(p<0.05\), marked with an asterisk), namely the rhythmic features tempo and pulse clarity, and the spectral features BE 400-800, BE 800-1600, BE \(>\) 1600, skewness, and kurtosis. The skewness and kurtosis measure the asymmetry and flatness of the spectrum around its mean value, respectively. The significant effects of these features indicate that these features are possibly related to the ability of the program to reveal spectral irregularities in the low-frequency range.