## 4 Experiments and Performance Evaluation
### Implementation Details  
Due to the large scale of the training dataset and the limited memory capacity of the experimental hardware, a custom data generator was developed to preprocess data in batches and feed it incrementally into the model. This approach ensures a stable and efficient training process while preventing system memory overflow. The multi-scale STFT magnitude processing has been described in detail in Section 3.3. Subsequently, multi-scale short-time Fourier transform features capturing both high- and low-frequency components are extracted and concatenated to form an enhanced magnitude spectrum, which serves as the model input. This input is then fed into the proposed TSDCA-BA model for training, with the log-MSE loss function applied. Additionally, the PESQ per  
Figure 6: Circuit diagram of the improved simple hearing aid. ceptual callback mechanism described in Section 3.4 is employed to compute the average PESQ score on the validation set after each epoch. The final model is selected based on the highest PESQ score, thereby improving the subjective perceptual quality of the speech enhancement. To capture both global and local spectral patterns, a multi-scale short-time Fourier transform (STFT) is applied. Specifically, two STFT settings with frame lengths of 512 and 256 samples are employed to extract spectral features at different temporal resolutions. Each scale uses a Hann window to perform the transformation, enabling the model to effectively capture both coarse and fine-grained spectral information. Subsequently, multi-scale short-time Fourier transform (STFT) is employed to extract both high-frequency and low-frequency components, which are then concatenated to form the enhanced spectral magnitude representation as the model input. This input is fed into the proposed TSDCA-BA model for training, using log-MSE as the objective function. Additionally, following the PESQ-aware callback strategy introduced in Section 3.2, we evaluate the model's perceptual quality on the validation set after each epoch by computing the average PESQ score. The final model is selected based on the highest PESQ performance, ensuring optimal perceptual enhancement. During inference, the noisy speech signal is first subjected to a pre-emphasis operation to enhance high-frequency components, consistent with the procedure used during training. Subsequently, multi-scale short-time Fourier transform (STFT) is applied to extract log-magnitude spectra at two different temporal resolutions. These spectra are temporally aligned and concatenated to form a unified spectral representation, which serves as the input to the neural network. To improve input stability, the concatenated features are normalized using their mean and standard deviation. The normalized features are then fed into the pre-trained model, which outputs a time-frequency mask designed to suppress noise while preserving speech components. The predicted mask is applied to the linear-scale magnitude spectrum to attenuate noise components. The phase information from the primary STFT scale is used to reconstruct the complex spectrogram. Finally, inverse STFT is performed to transform the enhanced spectrogram back into the time-domain waveform. A final normalization step is applied to the output audio to ensure appropriate amplitude scaling for subsequent analysis and evaluation. Inspired by [32], we conducted additional fine-tuning on the initial model to improve its performance under low signal-to-noise ratio (SNR) audio conditions. Specifically, audio samples with SNR below 0 dB were selected from the original training set to form a fine-tuning dataset. The learning rate was reduced to \(1.5\times 10^{-4}\) to ensure training stability. Multiple rounds of fine-tuning were performed, and the model achieving the highest PESQ score on the validation set was selected. Final performance was evaluated on a blind test set to determine the optimal model.