# Noise Modeling to Build Training Sets for Robust Speech Enhancement
## 4 Experiments and Discussion  
In order to compare our noise modeling-based synthesized datasets against finite noise synthesis datasets, we adopt quantitative measures and employ the \"GAN-train\", and \"GAN-test\" approaches to judge the competitor methods. \"GAN-train\" and \"GAN-test\" were first proposed by [PERSON] to measure the quality of data generated by GANs by calculating the distance between the generated samples and a real data manifold in terms of precision and recall [15]. The \"GAN-train\" refers to training the SE model on our synthetic training sets and evaluating its performance on real noisy samples. The synthetic samples are considered sufficiently diverse if an SE model trained on them can provide appealing noise reduction on a real noisy speech. The \"GAN-test\" utilizes an SE model trained on real training sets but tested on synthetic samples. Here, if an SE model trained on a real training set can provide appealing noise reduction on a noisy synthetic speech, the generated samples are considered a realistic approximation of the natural samples' (unknown) distribution. The \"GAN-train\" and \"GAN-test\" experiments aim to measure the diversity and similarity between synthetic and real noisy samples and to assess the extent to which NM-GAN can match real noise distributions. The experimental setup involves two groups of comparative \"GAN-train\" and \"GAN-test\" experiments. In the first \"GAN- train\" experimental group, the SE model is trained on our synthetic training set but tested on real noisy samples. In the first \"GAN- test\" experimental group, the SE model is trained on a real training set but tested on our synthesized noisy samples. In contrast, in the second group of \"GAN-train\" experiments, we employ the \"limited\" training set based on the direct synthesis of speech and limited real noise samples to train the same SE model and then test it on the real noisy set. Similarly, for the second group of \"GAN-test\" experiments, the SE model is trained on the real training set and tested on the \"limited\" set. In order to perform the two \"GAN-train\" and \"GAN-test\" comparative experiments, we prepare three different training sets. The first is a real-world recording, referred to as the real training set. The second training set is a synthesized set using limited real noise samples, referred to as the \"limited\" training set, while the third is created using our proposed method. In the first group of \"GAN-train\" experiments, an SE model is trained on our synthesized training set but tested on a set of real noisy samples. The preparation method of the three datasets is illustrated in Figure 5.  
The noisy speech recordings of the real training set are undertaken in a laboratory with almost no external interference. The laboratory's volume is 80 m\({}^{3}\) with a reverberation time of 0.45\(-\)0.6 s (500 Hz). Two loudspeakers are placed in the same horizontal position, where one played a pure voice and the other played noise. A recorder 0.5 m away from each loudspeaker recorded the noisy voice. For the entire process, apart from the speakers  
Figure 5: Preparation method of the three datasets. playing the voice and the noise, the entire laboratory is quiet without interference from other sound sources. Moreover, we adjust both loudspeakers' volumes to obtain noisy speech with different SNRs (\(-5\), \(0\), and \(5\) dB), providing \(150\),\(000\) real noisy speech samples under these SNR levels. We randomly select \(8000\) noisy samples from each SNR, which with the \(150\),\(000\) pure speech samples from the \"limited\" noisy speech for each SNR (one noise sample is reused), and from this we construct the \"limited\" training set. At the same time, the \(8000\) selected samples are used to train NM-GAN to generate \(150\),\(000\) noise samples, which are then synthesized with pure speech according to the different SNRs. Ultimately, we create three datasets, each containing \(150\),\(000\) sample pairs for each SNR.