## 4 Experiments
### Music Identification Experiment
#### 4.2.4 Music Identification Results  
In order to fairly compare with other methods, we did not use data augmentation or Wiener filter for the separation system. Table 3 shows the SDR, SIR, and SAR performance of the separated music and speech signals by each separation architecture. In the music separation result, Wave-U-Net performs best in SIR performance. SIR is a quantification of the degree of interference between speech and music signals, which shows how much speech signal remains in the separated music signal. Other structures except Wave-U-Net estimate the spectrogram magnitude of the music and reconstruct the signal using the phase information of the mixed signal. Using the phase information of the mixed signal causes the SIR to be lowered. To avoid this interference, Wave-U-Net estimates the signal directly in the time domain. However, in terms of SDR, MDenseNet, MMDenseNet, MMDenseLSTM, and the proposed architecture using DenseNet show higher performance than Wave-U-Net. Since SDR is a comprehensive value considering both SIR and SAR, separation performance is usually compared based on SDR. The proposed architecture \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Separated Signal** & \multicolumn{4}{c}{**Music**} & \multicolumn{4}{c}{**Speech**} \\ \hline
**SNR** & \multicolumn{2}{c}{**0 dB**} & \multicolumn{2}{c}{**-10 dB**} & \multicolumn{2}{c}{**0 dB**} & \multicolumn{2}{c}{**-10 dB**} \\ \hline
**ArchitectureStatistics** & **SDR** & **SIR** & **SAR** & **SDR** & **SIR** & **SAR** & **SDR** & **SIR** & **SAR** & **SDR** & **SIR** & **SAR** \\ \hline U-Net & 6.24 & 11.51 & 8.54 & 3.18 & 10.72 & 4.63 & 5.66 & 9.10 & 9.36 & 13.65 & 16.97 & 17.04 \\ Wave-U-Net & 6.33 & **14.19** & 7.54 & 2.97 & **13.15** & 3.70 & 6.02 & 10.32 & 8.97 & 14.24 & 18.70 & 16.66 \\ MDenseNet & 6.98 & 13.23 & 8.63 & 3.84 & 11.32 & 5.14 & 6.57 & 10.80 & 9.42 & 14.45 & 18.63 & 17.01 \\ MMDenseNet & 7.15 & 13.43 & 8.86 & 4.04 & 11.48 & 5.35 & 6.77 & 10.92 & 9.58 & 14.53 & 18.74 & 17.07 \\ MMDenseLSTM & 7.40 & 13.34 & 9.22 & 4.13 & 11.21 & 5.54 & **7.68** & **12.36** & **10.02** & 14.98 & **19.41** & 17.33 \\ Proposed & 7.72 & 13.74 & **9.45** & **4.44** & 11.72 & 5.72 & 7.63 & 12.29 & 9.98 & **15.00** & 19.39 & **17.34** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Separation results (dB) on broadcast contents. showed the best separation performance as 7.72 dB SDR at 0 dB SNR and 4.44 dB SDR at \(-\)10 dB SNR. At \(-\)10 dB SNR, Wave-U-Net showed lower separation performance than U-Net. Except for this case, the performance of each architecture showed reasonable results, as can be seen in previous and other studies [19; 37]. In the speech separation result, we can see that unlike music, the SIR performance of Wave-U-Net is lower than that of DenseNet-based separation architectures. the speech signal separation is less affected by the phase of the mixed signal than the music signal separation. We can see that the separation performance of MMDenseLSTM and the proposed architecture is similar and is better than other architectures. However, in music identification, the proposed architecture outperformed MMDenseLSTM. Table 4 shows the accuracy of music identification. The performance of the _Mix_ is the lower boundary, and the performance of the _Oracle_ is the upper boundary. The performance of the _Oracle_ is not 100% because of the distortion caused by the down-sampling. U-Net showed the lowest identification performance, and the proposed architecture showed the best performance in identification with 71.91% identification accuracy at 0 dB SNR and 48.03% identification accuracy at \(-\)10 dB SNR. However, some results did not correlate with SDR performance. MMDenseNet had higher separation performance than Wave-U-Net and MDenseNet, but lower identification performance. Figure 10 shows the music identification results and the fingerprinting features at the spectrogram for the query signal of each separation system.