# Environmental Attention-Guided Branchy Neural Network for Speech Enhancement
## 3 Experiments and Results
### Performance Comparison with Other Environment-Aware Methods  
The proposed method was compared with the DNN-based approaches without prior noise information for performance evaluation. However, the performance difference between the proposed method and other environment-related speech enhancement algorithms is unknown. In this section, we compare the denoising performance of the proposed speech enhancement approach (CGBNN-5) with three classic environment-aware algorithms in the specific application scenarios. The noise classification-based minimum mean square error speech estimator (MMSE) [25] and the optimally modified log-spectral amplitude (OMLSA) speech estimator [26] were evaluated to compare speech quality and intelligibility. Both of them were tested in the same application scenario with four known noise interferences. These two methods are denoted as NC-MMSE and NC-OMLSA in Figure 3. Additionally, the separate DNN denoising method with noise classification [29] (denoted as NC-DNN) was tested for performance comparison purposes. Figure 3 shows the averaged PESQ and STOI results of the above environment-aware methods in our test dataset. An intuitive comparison of the reconstructed speech spectrogram between the proposed CGBNN-5 and three comparing algorithms in four specific noise environments are shown in Figure 4. The comparison results of DNN model sizes are listed in Table 2. The model sizes were normalized by the NC-DNN model to make the comparison look more obvious. Figure 2: Averaged perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI) results obtained for noisy and deep neural network (DNN) enhanced speech in seen and unseen noisy environments. (**a**) PESQ in seen noises; (**b**) PESQ in unseen noises; (**c**) STOI in seen noises; (**d**) STOI in unseen noises. Figure 4: The spectrograms of noisy speech (degraded by four known noises in 0 dB signal-to-noise ratio (SNR) case), clean speech, and enhanced speech using NC-MMSE, NC-OMLSA, NC-DNN, and CGBNN-5. Figure 3: Averaged PESQ and STOI results for environment-aware methods with four known (babble, factory1, destroyer engine, and destroyer operation) noise interferences in a specific application scenario. (a) Averaged PESQ results; (b) Averaged STOI results. The averaged PESQ results, in Figure 3, demonstrate that the proposed CGBNN-5 outperforms the other environment-aware methods in SNR ranges from \(-5\) dB to \(15\) dB. Compared with NC-MMSE and NC-OMLSA, the average PESQ of CGBNN-5 increased by \(16.22\%\) and \(12.02\%\), respectively, in four specific noise environments. The PESQ value of CGBNN-5, in this paper, achieved an averaged improvement of \(3.8\%\) over the state-of-the-art method, NC-DNN. According to the STOI results in Figure 3, the performance improvement of CGBNN-5 and NC-DNN is significantly better than that of NC-MMSE and NC-OMLSA. The CGBNN-5 achieved an averaged STOI improvement of \(12.3\%\) and \(14.75\%\) over NC-MMSE and NC-OMLSA, respectively, in the specific application scenario. Compared with NC-DNN, CGBNN-5 did not show great advantages, but it also achieved a \(1.74\%\) increase in STOI. Although the averaged STOI score of CGBNN-5 in \(15\) dB SNR is not the best, its scores in the range from \(-5\) dB to \(10\) dB are still significantly better than the other three methods, that is, the proposed branchy model with prior noise information can obtain better speech quality and intelligibility than the other three methods in specific application scenarios. Furthermore, according to the spectrogram details of enhanced speech in Figure 4, it is clear that the DNN-based denoising methods (NC-DNN and CGBNN-5) achieve less speech distortion and retain less residual noise as compared with statistical-based approaches (NC-MMSE and NC-OMLSA). We also calculated the signal-to-distortion ratio (SDR) [39] to measure the speech distortion introduced by the denoising algorithms.