# Employing Huber and TAP Losses to Improve Inter-SubNet in Speech Enhancement
## 4 Experimental Setup  
In order to evaluate the efficacy of the presented method, we employed the VoiceBank-DEMAND database and task [40; 41], which combine the VoiceBank speech dataset with noise sources from the DEMAND database. The training dataset consisted of 11,572 utterances collected from 28 speakers. As for the test dataset, it included 824 utterances spoken by only 2 speakers. In the training set, the utterances underwent corruption by ten types of noise from the DEMAND database. The entire DEMAND dataset included a variety of noise types categorized into six main groups (Residential, Nature, Office, Public, Street, and Transportation). Each category consisted of three specific scenes, totaling 18 distinct noise scenes, providing recordings that reflect various real-world acoustic environments. These noises were introduced to the training set at four distinct signal-to-noise ratios (SNRs): 0, 5, 10, and 15 in dB. Conversely, the test set contained utterances affected by five different types of DEMAND noise, present at four different SNRs: 2.5, 7.5, 12.5, and 17.5 in dB. Additionally, we reserved around 200 utterances from the training set for validation purposes. For the training of the Inter-SubNet framework, we referred to [34] and employed the following parameter settings:  
* A Hanning window with 32 ms frame size and 16 ms frame shift was applied to input signals to obtain the spectrogram. * The sub-band unit was set to have \(2N+1=31\) frequency bins, and the number of frames \(T\) for the input-target sequence pairs during training was set to 192. * For the first SIL block, the SubInter module and the LSTM contained 102 hidden units and 384 hidden units, respectively. For the second SIL block, the SubInter module and the LSTM contained 307 hidden units and 384 hidden units, respectively. * The batch size was reduced from the initial value of 20 in the script to 4 to accommodate the GPU constraints of the servers in our laboratory. Following [34], the TAP estimator used in TAP loss was set to be a 3-layer bi-LSTM with 256 hidden units, and it was trained on the DNS 2020 clean dataset [42]. As for the employed Huber loss, the hyperparameter \(\Delta\) in Equation (7) was set to 1. According to [34], the FullSubNet framework trained with a loss function that includes cIRM-wise MSE loss and a 0.03-weighted TAP loss yielded the best results. Therefore, we varied the weights of the TAP loss, \(\lambda_{1}\) in Equation (14) and \(\lambda_{2}\) in Equation (15), within the range of \([0.01,0.05]\) in increments of 0.005 to evaluate the performance of the trained Inter-SubNet. The experiments were conducted using the PyTorch programming language on a desktop computer featuring an AMD Ryzen 5 3550X 6-Core Processor (Santa Clara, CA, USA) and an NVIDIA GP102 GPU (Santa Clara, CA, USA). To evaluate Inter-SubNet with different loss function settings in SE performance, we employed several objective metrics:  
1. **Wideband Perceptual Evaluation of Speech Quality (WB-PESQ)**[36]: This metric ranks the level of enhancement for wideband speech signals, typically sampled at frequencies of 16 kHz or higher. WB-PESQ indicates the quality difference between the enhanced and clean speech signals, and it ranges from \(-0.5\) to \(4.5\). 2. **Narrowband Perceptual Evaluation of Speech Quality (NB-PESQ)**[37]: Similar to WB-PESQ, NB-PESQ evaluates the quality of speech signals but is limited to narrowband ones, typically sampled at 8 kHz. WB-PESQ involves high-fidelity speech processing, whereas NB-PESQ is more suited for traditional telephony. 3. **Short-Time Objective Intelligibility (STOI)**[38]: This metric measures the objective intelligibility for short-time time-frequency (TF) regions of an utterance with discrete Fourier transform (DFT). STOI ranges from 0 to 1; a higher STOI score corresponds to better intelligibility. 4.