# DIFFBAS: An Advanced Binaural Audio Synthesis Model Focusing on Binaural Differences Recovery
## 4 Discussion  
DNN-based modeling technology has been proven effective for binaural audio modeling and captures nonlinear effects in dynamic sound propagation that traditional DSP methods cannot handle. Currently, there are three neural network types used for BAS, including WarpNet [15], NFS [16] and BinauralGrad [17], which have shown that synthesized binaural audio is superior to traditional DSP methods in terms of clarity and realism in subjective and objective evaluations. However, the potential of neural networks in modeling binaural audio spatial information has not yet been fully explored. In order to further improve the ability of the neural network-based BAS method to synthesize binaural audio spatial information, we propose DIFFBAS. This method avoids over-fitting problems caused by the diversity of environments and sound sources by focusing on modeling sound propagation paths, thereby improving the accuracy of synthesizing binaural audio. Combined with the characteristics of human auditory perception, we introduced the IPD loss as a training loss for the first time in related tasks to effectively supervise the learning of interaural phase difference information, which is highly related to the spatial information in binaural audio.  
Our method has significant improvements over existing baseline models. In terms of binaural difference recovery, compared with WarpNet, the IPD error of DIFFBAS + WarpNet  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**WarpNet [15]**} & \multicolumn{3}{c}{**NFS [16]**} \\ \hline
**Condition** & \(\mathcal{L}_{2}\times\mathbf{10^{3}}\downarrow\) & \(Amp\downarrow\) & \(\mathcal{L}_{\textit{plb}}\downarrow\) & \(\mathcal{L}_{\textit{IPD}}\downarrow\) & \(\mathcal{L}_{2}\times\mathbf{10^{3}}\downarrow\) & \(Amp\downarrow\) & \(\mathcal{L}_{\textit{plb}}\downarrow\) & \(\mathcal{L}_{\textit{PO}}\downarrow\) \\ \hline Baseline & 0.145 & 0.036 & 0.815 & 1.064 & 0.163 & 0.040 & 0.869 & 1.250 \\ \(\mathbf{x}_{mono}\)-\(\mathcal{L}_{IPD}\) & 0.142 & 0.036 & 0.819 & 0.967 & 0.177 & 0.047 & 0.824 & 1.194 \\ \(\mathbf{x}_{mono}\)-\(\mathcal{L}_{IPD}\) & 0.033 & 0.018 & 0.358 & 0.865 & 0.049 & 0.019 & 0.434 & 1.087 \\ \hline DIFFBAS & **0.033** & **0.018** & **0.358** & **0.834** & **0.049** & **0.018** & **0.431** & **1.083** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of binaural audio synthesis quality under different modules. The \(\downarrow\) symbol indicates that the lower the metric, the better the effect. The optimal value of each metric is shown in bold.  
is significantly reduced from 1.064 to 0.834. Similarly, the IPD error of DIFFBAS + NFS is also reduced from 1.250 to 1.083. These results not only prove the effectiveness of our method but also show that it is suitable for different backbone networks, providing new research ideas for DNN-based binaural audio synthesis tasks.  
Furthermore, our study is also limited by the scarcity of dynamic continuous binaural audio datasets, which makes it difficult for the model to account for the generalization ability of audio rendering beyond the frequency domain of the training set. Additionally, we are unable to objectively assess sounds distributed across different frequency ranges. However, we have tested different types of sounds such as music, dog barking, and car horns into the trained model. The synthesized binaural audio can obtain a sense of space consistent with the input position information in the auditory experience. This shows to a certain extent that our method also has a certain generalization ability when dealing with other types of sounds.