# Integrating Dilated Convolution into DenseLSTM for Audio Source Separation
## 4 Experiments
### Music Experiment
#### 4.2.3 Experimental Results for Music Signal Separation and Identification
We investigate whether such distortion interferes with fingerprint extraction and how much such distortion occurs in each deep learning architecture as the quantitative indicator. To express the effect of this distortion as the quantitative indicator, the average number of all fingerprints across the frequency boundaries (\(AcrAF\)) and the average number of matched fingerprints across the frequency boundaries (\(AcrMF\)) were measured as shown in Table 9. In our experiment, deep learning architectures in which many parameters were placed independently in each frequency band were MMDenseNet, MMDenseLSTM, and the proposed architecture, and the frequency band boundaries were 2 and 4 kHz. MMDenseNet had the smallest value in these two indicators, which could explain why MMDenseNet showed higher separation performance than Wave-U-Net and MDenseNet \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multicolumn{2}{c}{**0 dB MSR**} & \multicolumn{2}{c}{**–10 dB MSR**} \\ \cline{2-5}  & **Median SDR** & **Mean SDR** & **Median SDR** & **Mean SDR** \\ \hline U-Net & 6.24 & 6.19 & 3.18 & 3.11 \\ \hline Wave-U-Net & 6.33 & 6.22 & 2.97 & 2.86 \\ \hline MDenseNet & 6.98 & 6.86 & 3.84 & 3.67 \\ \hline MMDenseNet & 7.15 & 7.10 & 4.04 & 3.91 \\ \hline MMDenseLSTM & 7.40 & 7.38 & 4.13 & 4.05 \\ \hline DilDenseNet & **7.72** & 7.63 & 4.44 & 4.32 \\ \hline Proposed & 7.69 & **7.65** & **4.54** & **4.40** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Music separation performance measured in signal-to-distortion ratio (SDR) (dB) on broadcast contents. MSR: music-to-speech ratio. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multicolumn{2}{c}{**0 dB MSR**} & \multicolumn{2}{c}{**–10 dB MSR**} \\ \cline{2-5}  & **MI Accuracy** & **MF** & **MI Accuracy** & **MF** \\ \hline Unprocessed & 43.38 & 20.2 & 4.59 & 11.8 \\ \hline U-Net & 42.33 & 16.0 & 19.38 & 11.8 \\ \hline Wave-U-Net & 54.77 & 19.1 & 26.89 & 12.4 \\ \hline MDenseNet & 52.57 & 19.1 & 28.44 & 13.2 \\ \hline MMDenseNet & 49.66 & 17.4 & 26.67 & 12.3 \\ \hline MMDenseLSTM & 67.94 & 22.0 & 44.96 & 14.9 \\ \hline DilDenseNet & 71.91 & 23.8 & 48.03 & 15.5 \\ \hline Proposed & **73.35** & **25.0** & **50.75** & **16.1** \\ \hline Oracle & 95.92 & 72.6 & 95.92 & 72.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: MI accuracy (%) and the average number of matched fingerprints (\(MF\)) in the identified queries. has a dilated block that effectively increases the receptive field in consideration of acoustic characteristics in the spectrogram. In the speech recognition experiment after speech enhancement, subjective evaluation was performed on the enhanced speech, and various objective indicators (PESQ, SDR, STOI, CSII, NCM, and WER) were measured. Encoder-decoder style architectures, MMDenseLSTM, DiDenseNet, and the proposed architecture showed better performance with fewer parameters than GRN. In addition, it was confirmed that simply increasing the number of parameters did not improve performance. The speech recognition performance of WER had a highest correlation with NCM than with other indicators, and the proposed architecture in the preference score, PESQ, STOI, CSII, NCM, and WER showed the best performance compared to other deep learning architectures. In the music identification experiment after music signal separation, the performance of separation was measured using SDR. Although SDR and identification accuracy did not have a correlation in all deep learning architectures, the proposed architecture showed the best performance compared to other deep learning architectures in SDR performance and identification performance. In addition, when the fingerprints of the query and reference were plotted overlapped, it was confirmed that more fingerprints are matched over wider areas in the proposed architecture than in the MMDenseLSTM.