## 4 Experimental Setup, Results, and Discussion
### Experimental Setup  
We first use the VoiceBank-DEMAND [41] task for evaluation, which contains the VoiceBank speech dataset [42] and the noise sources from the DEMAND database [43]. As for the Voicebank dataset, the training set includes 11,572 utterances from 28 speakers, and the testing set includes 824 utterances from 2 speakers. The utterances in the training set are corrupted by ten types of noise from the DEMAND database at four different signal-to-noise ratios (SNRs): 0, 5, 10, and 15 dB. The test set is contaminated by five types of DEMAND noise at four SNRs: 2.5, 7.5, 12.5, and 17.5 dB. In particular, we set aside around 200 utterances from the training set for validation, which are fewer than the common alternative, 742 utterances, for validation. The initial idea is that we expect such a choice to make the learned model less specific to the DEMAND noise scenario, allowing it to be generalized and well employed in an unseen noise scenario (such as QUT as introduced in the following). To further investigate the generalization capability of the presented methods, we have built another test set. This test set uses the same (noise-free) speech utterances as the original test set, while different types of non-stationary noise from the QUT-NOISE dataset [44] corrupt them to simulate a more severe noisy scenario. The QUT-NOISE dataset includes 5 noise types: cafe, home, street, car, and reverb. The resulting noisy utterances are at any of \(-\)5, 0, 5, and 15 dB of SNRs. Notably, all of the waveforms used for the experiments are resampled at 16 kHz. The db2 wavelet function is used to implement the DWT in our methods. Wavelet db2 consists of 4 points only, making the convolution process (filtering) quite simple and uncomplicated. Wavelet db2 is also one of the most commonly used wavelet functions as its support is small enough to separate the features of interest [45]. Figure 13: The diagram of integrating three DWT features with an MPF module with inter-channel softmax. As for the Conv-TasNet, we set the configuration of the mask-estimation network with the following hyperparameters: the number of convolutional blocks in each repeats \(X=8\) times, the number of repeats \(R=3\) times, the number of channels in the bottleneck and the residual paths' \(1\times 1\)-conv blocks is \(B=128\), the number of channels in convolutional blocks is \(H=256\), the number of channels in skip-connection paths' \(1\times 1\)-conv blocks is \(S_{c}=128\), and the kernel size in convolutional blocks is \(P=3\), which is half the setting of \(H\) in [18]. As for the hyperparameter settings for the model training, we employ an Adam optimizer, a learning rate of \(10^{-3}\), a weight decay of \(10^{-5}\), a batch size of 3, and 100 epochs. The used loss function for training Conv-TasNet and DPTNet is the scale-invariant signal-to-noise ratio (SI-SNR) [46]. To evaluate the denoising capability of the SE methods, we employ three objective metrics:  
1. Perceptual estimation of speech quality (PESQ) [47]: This metric ranks the level of enhancement for the processed utterances relative to the original noise-free ones. PESQ indicates the quality difference between the enhanced and clean speech signals, and it ranges from \(-0.5\) to \(4.5\). Briefly speaking, the calculation of PESQ is achieved in several stages: time alignment, level alignment to a calibrated listening level, time-frequency mapping, frequency warping, and compressive loudness scaling. 2. Short-time objective intelligibility (STOI) [48]: This metric measures the objective intelligibility for short-time time-frequency (TF) regions of an utterance with discrete-time Fourier transform (DFT). STOI ranges from 0 to 1, and a higher STOI score corresponds to better intelligibility. Briefly speaking, the STOI of the processed utterance \(\$\) with respect to its clean counterpart \(\$\) is calculated with the following procedures: First, we perform STFT on \(\$\) and \(\$\) to obtain their spectrograms, \(\hat{X}\) and \(X\).