## 3 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Input**} & \multirow{2}{*}{**Params**} & \multicolumn{3}{c}{**Accuracy (\%)**} \\ \cline{4-5}  & & & \(\infty\) & **0 dB** & **diff.** \\ \hline
[17] & MFCC & - & 51.2 & 32.1 & \(-19.1\) \\
[18] & WPCC & - & 70 & 30 & \(-40\) \\
[7] & MFCC & - & 72.3 & 48.0 * & \(-24.3\) \\
[19] & 6552 vector & 19.7 M & 62.7 & 47.2 & \(-15.5\) \\ This work & waveform & 4.2 M & 66.2 & 62.4 & \(-3.8\) \\ \hline \hline \end{tabular} \({}^{*}\)1.4 dB SNR.  
\end{table} 
Table 3: Performance comparison of speech emotion recognition models under noisy environments at different SNR values, diff. is the accuracy difference between clean (\(\infty\)) and 0 dB SNR. To assess the effect of the four band-pass waveforms used for input and ground-truth on the accuracy, the accuracy values of the three models are compared in Table 5. The six-decoder model (proposed model) uses the four band-pass filter waveforms (\(x_{bpf}\)) as input and generates four band-pass waveforms (\(y_{1}\), \(\hat{y}_{2}\), \(\hat{y}_{4}\), and \(\hat{y}_{8}\)) and a pitch-related waveform (\(\hat{y}_{p}\)) to compare with the ground-truth waveforms. Out of the six decoders, five decoders generate the above-mentioned five waveforms (\(y_{1}\), \(\hat{y}_{2}\), \(\hat{y}_{4}\), \(\hat{y}_{8}\), and \(\hat{y}_{p}\)) and the remaining one decoder generates the emotion classifier input waveform (\(\hat{y}_{emo}\)). The three-decoder model uses the raw speech waveform as input and generates a speech waveform and a pitch-related waveform to compare with the ground-truth waveforms. Out of the three decoders, two decoders generate a speech waveform and \(\hat{y}_{p}\) and the remaining one decoder generates the emotion classifier input waveform (\(\hat{y}_{emo}\)). The two-decoder model uses the raw speech waveform as input and generates a speech waveform to compare with the ground-truth waveform. Out of the two decoders, one decoder generates a speech waveform, and the other decoder generates the emotion classifier input waveform (\(\hat{y}_{emo}\)). On average, the accuracy of the six-decoder model (proposed model) is better than the three-decoder model and the two-decoder model by 4.3% and 5.7%, respectively. The pitch-related waveform enhanced the accuracy by 1.4% on average. The ablation study was performed to find out the significance of each of the five waveforms (\(\hat{y}_{1}\), \(\hat{y}_{2}\), \(\hat{y}_{4}\), \(\hat{y}_{8}\), and \(\hat{y}_{p}\)) used in the proposed model. Table 6 lists the accuracy values at different SNR values for the proposed model and the models where one of the five waveforms is removed from the proposed model; to remove a waveform from the proposed model, the corresponding decoder is also removed from the proposed model. Table 7 presents the averaged accuracy of the ablation study models (Table 6); the accuracy values of each model were averaged w.r.t. SNR to obtain the averaged accuracy. The accuracy degradation data compared to the proposed model reveals that the 0\(\sim\)1 kHz waveform is the most important, and the 1 kHz\(\sim\)2 kHz waveform is the second most important. Besides, the high-frequency waveforms (2 kHz\(\sim\)4 kHz and 4 kHz\(\sim\)8 kHz) play substantial roles in emotion recognition. The pitch-related waveform is also helpful in enhancing the accuracy. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{6}{c}{**Accuracy (\%)**} \\ \cline{2-7}  & \(\infty\) & **20 dB** & **5 dB** & **0 dB** & **\(-\)3 dB** & **\(-\)6 dB** \\ \hline Proposed & 66.2 & 65.5 & 63.8 & 62.4 & 61.0 & 59.0 \\ w/o 0\(\sim\)1 kHz & 60.7 & 60.5 & 59.9 & 57.5 & 56.4 & 53.5 \\ w/o 1 kHz\(\sim\)2 kHz & 61.9 & 61.4 & 60.0 & 58.7 & 57.1 & 54.9 \\ w/o 2 kHz\(\sim\)4 kHz & 63.2 & 62.3 & 61.9 & 60.8 & 59.6 & 57.2 \\ w/o 4 kHz\(\sim\)8 kHz & 62.1 & 62.5 & 62.2 & 60.8 & 59.0 & 56.6 \\ w/o pitch & 63.8 & 63.4 & 61.8 & 60.6 & 59.5 & 57.5 \\ \hline \hline \end{tabular}
\end{table}