# Integrating Dilated Convolution into DenseLSTM for Audio Source Separation
## 4 Experiments
### Speech Experiment
#### 4.1.3 Experimental Results of the Subjective Quality Measure for Speech Enhancement  
We tested four models for speech enhancement: GRN [35], MMDenseLSTM [31], DiDenseNet [33], and the proposed architecture. In several recent studies [55; 56], GRN proposed for speech enhancement was experimented for performance comparison. Deep learning models were implemented by ourselves because there was no open code. In DiDenseNet which we designed in the previous experiment [33], we did not reverse the feature map in the multi-band block for speech enhancement and deleted the multi-band block in the decoding process. In addition, dividing the band at \(2\) kHz and \(4\) kHz was equally applied to DiDenseNet, MMDenseLSTM, and proposed architecture. It was confirmed by experiments that these modifications showed better performance in speech enhancement.  
For the subjective listening evaluation of the speech quality, we conducted a relative preference test [57] targeting GRN, MMDenseLSTM, DiDenseNet, and the proposed architecture. From the four models, we obtained six combinations of model for comparison. The subjects participated in a total of \(18\) conditions (six combinations of model for comparison, three different noise and music levels). Each condition had five pairwise comparisons and the subject performed a total of \(90\) pairwise comparisons. To prevent subjects from predicting information, such as the condition and speech material of each sample, we provided the condition and speech material in random order, and the sample length used for each preference test was \(2\)-\(3\) s. In addition, each subject independently conducted the subjective evaluation in order not to share their opinions with each other. In each pairwise comparison, a mixture and pairs of enhanced speech samples, which resulted from the comparison models, were provided, and the mixture was always heard first.  
As a total of 30 subjects participated in the subjective listening test, 150 preference results were obtained for each condition. The listeners had the ability to select a preferred sample (1 score) or \"can't decide\" (0.5 score), and the preference score and significance were calculated by combining the preference results of all listeners. We determined the preference score by applying the average to the preferred frequency. In addition, we calculated the one-tailed significance of the binomial test.  
Table 3 shows the preference scores of the subjective speech quality listening test for the proposed architecture and other deep learning architectures. In the subjective evaluation, the speech quality of the proposed architecture was evaluated better than DiDenseNet, MMDenseLSTM, and GRN. DiDenseNet and MMDenseLSTM were evaluated to have the same speech quality. GRN was evaluated to have the lowest speech quality among deep learning architectures. In addition, the comparisons including GRN showed high significance in all conditions, which indicated that the difference in performance from other deep learning structures was clear. Likewise, \"Total\" results of all comparisons other than the DiDenseNet and MMDenseLSTM comparisons showed high significance and thus represent reliable results.