A wavelet network-based speech enhancement system using noisy-as-clean strategy

In recent years, the field of speech enhancement has greatly benefited from the rapid development of neural networks. However, the requirement for large amounts of noisy and clean speech pairs for training limits the widespread use of these models. Wavelet network-based speech enhancement typically relies on clean speech signals as a training target. This paper presents a new method that combines a neural network with the wavelet theory for speech enhancement without the need for clean speech signals as targets in training mode. Five wide evaluation criteria, namely short-time objective intelligibility (STOI), signal-to-noise ratio (SNR), segmental signal-to-noise ratio (SNRseg), weighted spectral slope (WSS) and logarithmic spectral distance (LSD), have been used to confirm the effectiveness of the proposed method. The results show that the proposed method performs similar to a wavelet neural network (WNN) trained with clean signals, or even superior to those obtained from the clean target-based strategies.