# Integrating Dilated Convolution into DenseLSTM for Audio Source Separation
## 4 Experiments
### Music Experiment
#### 4.2.3 Experimental Results for Music Signal Separation and Identification  
In order to compare the objective performance differences of deep learning architectures, we performed music signal separation and music identification experiments of several deep learning architectures in the same environment. The separation performance was SDR and was expressed using median and mean statistics. The music identification performance was the identification accuracy. The performance of other deep learning architectures except the proposed architecture was the same as the previous study [33]. In addition, we analyzed the fingerprint used in the music identification system. Since the fingerprint was composed of indexes, it could be plotted overlapped on the spectrogram, which helped intuitive interpretation. Table 7 shows the separation performance. The proposed architecture improved the separation performance of MMDenseLSTM, and it showed the best performance compared to other deep learning architectures. Table 8 shows the music identification (MI) performance and the average number of matched fingerprints (\(MF\)) in the identified queries. Likewise, the proposed architecture in which the dilated block was added to MMDenseLSTM improved the identification performance of MMDenseLSTM by 16.9% at 0 dB MSR and 10.5% at \(-\)10 dB MSR, relatively, and it showed the best performance compared to the existing deep learning architecture. The statistical significance test was confirmed through the SCTK toolkit in the MI results of Table 8. The proposed architecture showed the \(p\)-value less than 0.001 in all comparisons with other deep learning architectures. In the 0 dB, \(-\)10 dB MSR environment, the difference in performance between the proposed architecture and other deep learning architectures was statistically significant. Apart from the identification performance, \(MF\) showed how well the peaks were estimated. Overall, \(MF\) has correlation with the identification performance. However, at 0 dB MSR, the \(MF\) for the unprocessed signal was quite high considering the identification performance. This showed that the landmark-based fingerprinting scheme was robust to noise at 0 dB MSR. The proposed architecture showed the largest \(MF\) except _Oracle_ at 0 dB and \(-\)10 dB MSR. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**DB** & **Music** & **Speech** & **Remark** \\ \hline \multirow{3}{*}{Training} & extracted 1 sample & \multirow{3}{*}{1673 samples (5 h 30 min)} & Used for the training and validation of the separation model \\  & in each song & & \\  & (150 songs, 30 min) & & \\ \hline \multirow{3}{*}{Test} & extracted 2 samples & \multirow{3}{*}{1823 samples (6 h)} & Used for measuring the separation and identification performances \\  & in each song & & \\  & (7295 songs, 48 h) & & performances \\ \hline All & 9118 songs & 3646 samples & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Dataset configuration for music experiment. The sample length is 12 s.  
Figure 8 shows the fingerprints of the separated music signal from the MMDenseLSTM and the proposed architecture. In both results, the proposed architecture had a larger number of matched fingerprints than the MMDenseLSTM. This means that the peak points of the estimated music signal were better preserved in the proposed architecture than in MMDenseLSTM. In Result 1, the matched fingerprints of the low band were the same in the MMDenseLSTM and the proposed architecture, and more fingerprints were matched in the middle and high bands of the proposed architecture. In Result 2, the proposed architecture showed more matched fingerprints in wider areas of the middle and high bands than the MMDenseLSTM. In the previous study, we found that distortion occurs at the frequency band boundary of the output spectrogram when the deep learning architecture was designed by independently placing excessive parameters in each frequency band of the input spectrogram [33].