Sub-band Knowledge Distillation Framework for Speech Enhancement
Table 3: Demonstrates the effectiveness of the sub-band knowledge distillation framework. C represents the number of memory cells per layer in LSTM. #Params represents the number of parameters of the model.
['Model', 'C', '#Params (M)', 'PESQ', 'STOI (%)']
Since the input of the F model is the full-band features, the number of parameters of the F model is higher than that of the S1 model and S2 model. Whether the number of memory cells is 256 or 512, we can find some similar conclusions. (1) The performance of the S1 model is slightly worse than the F model. This may be because full-band input can bring more context information, which can help the model to capture the features on the spectrogram better. However, the performance gap is tiny, only 0.56 to 0.66% and 0.29 to 0.30% higher on PESQ and STOI, respectively. This may be because the S1 model can cover 40 frequency bands at a time, which is fairly sufficient to capture most of the local feature information. On the other hand, S1 has a 7% reduction in the parameter amount compared to the F model. Considering that the speech enhancement model is usually deployed offline on hardware, this slight performance degradation is normally acceptable. (2) With the guidance of the elite teacher model, the S2 model achieved better results than S1, which shows that the supervision of the teacher models is effective. It is worth mentioning that this improvement does not increase the number of parameters and the computational cost of the model. We also note that although the S2 model also does not have complete full-band context information, its performance noticeably exceeds model F.