## 2 The Datasets and Acoustic Features
### Acoustic Features  
The current music acoustic features that can be extracted mainly include the underlying physical features and high-level semantic features [30]. The underlying physical features are based on the time-frequency attributes of an audio signal, including spectral, temporal,and spectro-temporal features. High-level semantic features are abstract semantic features extracted by signal processing, image processing, and other methods, which can depict the inherent elements of music. In this study, we selected the acoustic features that had clear physical meanings to clarify the mechanism of emotional perception and classified them into four categories based on music elements: timbre, pitch, loudness, and rhythm. Although the physical meaning of MFCC is not very clear, many researchers have confirmed that it is an effective feature for MER. Therefore, it was also included in this research [31; 32]. The list of acoustic features is presented in Table 1.  
Essentia [33] and MIRtoolbox [34] were employed to extract acoustic features. Low-level acoustic features are based mostly on the global static features that describe the entire piece of music, yet the time-varying characteristics of low-level acoustic features are the main factors affecting the emotional perception of music. The features extracted through Essentia were computed over frames of approximately 25 ms with a frame step of 10 ms. In the MIRtoolbox, the frame size was 50 ms, and an overlap of 50% between successive frames was used. To reflect these time-varying characteristics, statistical values of features were introduced as an extension of static features. Essentia has algorithms for computing the mean, variance (var), mean of first-order difference (dmean), and variance of first-order difference (dvar), whereas MIRtoolbox calculates the mean, standard deviation (std), envelope, periodic frequency (PeriodFre), and period entropy (PeriodAmp) as time-varying statistics. There were 557 dimensions of acoustic features in total. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Category** & **Feature** & **Dimensionality** \\ \hline \multirow{8}{*}{Timbre} & Spectral characteristics—Centroid, Complexity, Decrease, & \\  & Entropy, Skewness, Kurtosis, RMS, Rolloff, Strongpeak, & \\  & Spread, Contrast Coeffs (1–6), Irregularity, Spectral Flux, High & \\  & Frequency Content (HFC) & \\ \cline{2-3}  & Spectral characteristics of ERB Bands—Crest, Flatness, & 20 \\  & Kurtosis, Skewness, Spread & \\ \cline{2-3}  & Temporal characteristics—Lowenergy, ZCR & 5 \\ \cline{2-3}  & Mel-frequency cepstral coefficients (MFCC) & 52 \\ \cline{2-3}  & Perception characteristic—Dissonance & 4 \\ \hline \multirow{8}{*}{Pitch} & Harmonic Pitch Class Profile (HPCP), HPCP Entropy & 148 \\ \cline{2-3}  & Harmonic Change Detection Function (HCDF) & 6 \\ \cline{2-3}  & Tuning-related characteristics—Tuning Diatonic Strength, & \\  & Tuning Equal Tempered Deviation, Tuning Frequency, Tuning & 4 \\  & Nontempered Energy Ratio & \\ \hline \multirow{8}{*}{Loudness} & Dynamic Complexity & 1 \\ \cline{2-3}  & Silence Rate (30 dB/60 dB) & 8 \\ \cline{2-3}  & ERB Bands Energy & 160 \\ \cline{2-3}  & Spectral Bands Energy—High, Middle High, & 16 \\ \cline{2-3}  & Middle Low, Low & \\ \hline \multirow{8}{*}{Rhythm} & Onset Rate, BPM, Tempo & 6 \\ \cline{2-3}  & BPM Histogram—First Peak BPM, First Peak Spread, First & \\ \cline{1-1}  & Weight, Second Peak BPM, Second Peak Spread, & 24 \\ \cline{1-1}  & Second Weight & \\ \cline{1-1} \cline{2-3}  & Beats Loudness Band & 24 \\ \cline{1-1} \cline{2-3}  & Danceability & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of acoustic features selected for our study. Timbre is a multi-dimensional property [35; 36]. For our study, five types of timbre features were extracted, as shown in Table 1. General spectral characteristics can describe the shape and structure of the signal spectrum. Spectral characteristics of ERB bands were chosen to show the characteristics of human auditory perception.