# Upmix B-Format Ambisonic Room Impulse Responses Using a Generative Model
## 4 Training Data  
The training of the network is carried out using a combination of simulated and measured RIRs. The evaluation of the model's performance is conducted using measured RIRs. The measured RIRs used in this study were recorded by [PERSON] et al. using the Eigenmike in reverberation chambers. This dataset comprises five source positions and seven receiver positions, repeated with five different room configurations that exhibit varying levels of reverberation. In total, the dataset consists of 102 sets of RIRs [29]. The reverberation time (RT60) of the rooms ranges from 250 to 800 ms. To prepare the data for training, the database was downsampled to a sampling rate of 16 kHz. To accommodate the resource limitations of the training server, we truncated the RIRs to include only the first 4096 data points. This truncated portion corresponds to a duration of 256 ms of the RIRs. This 256 ms segment was used for modeling and analysis in our research. While this truncation reduces the length of the RIRs, our experimental results demonstrate the excellent upmixing performance of the proposed method.  
We augmented our dataset by generating simulated RIRs using the SMIR toolbox [34]. The simulated dataset includes 35 different room acoustic configurations with variations in room size and wall reflection coefficients. The smallest room size in our dataset is \(3.9\) m \(\times\) \(5.1\) m \(\times\) \(2.8\) m, while the largest room size is \(12\) m \(\times\) \(15\) m \(\times\) \(3.5\) m. The reverberation time RT60 of the simulated RIRs ranges from 200 to 600 ms. In each of these room configurations, we placed the sound source at 10 different positions, allowing for variation in the source location within the room. Further more, we positioned 4 to 10 receivers within the room to capture the acoustic responses. Both the sound source and the microphones were placed on the same horizontal plane at a height of approximately 1.5 m. The RIRs were captured using a virtual rigid spherical microphone array with the same distribution as the Eigenmike, resulting in 4 th-order RIRs. The simulation setup involved using a sampling rate of 16 kHz and generating RIRs with a length of 4096 samples. In total, we simulated 1450 sets of RIRs.  
Both the measured and simulated microphone array RIRs were generated based on the scattered nature of the 4 th-order rigid sphere. The corresponding FOA signals can be derived by substituting \(N=1\) into Equation (10), and reconstructed using Equation (11) to align with the Eigenmike configuration. These reconstructed FOA signals, which contain only first-order sound field information, were utilized as the input signals of the model. The RIRs from both the simulated and measured databases were used as target signals for the model. The combined database used in this study consisted of 40 different room configurations, resulting in a total of 1552 sets of measured and simulated spherical array RIRs.  
During the training phase, the input first-order reconstructed RIRs and target signals were reshaped from dimensions of \(32\times 4096\) to \(16\),\(384\times 1\). The HiFi-GAN architecture, described in Section 3.2, was employed for the training process. For the dataset, we divided it into two distinct subsets, i.e., a training set and a test set. Specifically, in the case of the measured dataset, it was split with an 8:2 ratio. The simulated dataset was used exclusively as the training set. This division ensures that the model is evaluated on real-world measurements to assess its generalization and performance.