Abstract
This paper proposes a Deep Neural Network (DNN)-based Wiener gain estimator for speech enhancement. The proposal is in the framework of the classical spectral-domain speech enhancement algorithms. In this case, we used the Optimal Modified Log-Spectral Amplitude (OMLSA), but consider that this proposal could fit many alternative speech estimation algorithms. We determined the best usage of the DNN approach at learning a robust instance of the Wiener gain estimator according to the characteristics of the SNR estimation and the gain function. To design a DNN architecture adjusted for the speech enhancement task, we study various configuration issues frequently used in DNN-based solutions, including speech representations, residual connections, and causal vs. non-causal designs. Thus, we provide conclusions for the use of DNN architectures with the enhancement purpose. Experiments show that the proposal provides results on the state-of-the-art. But beyond the objective quality measures, there are examples of noisy vs. enhanced speech available for listening to demonstrate in practice the skills of the method in real audio.
Wiener Filtering in Frequency Domain to Enhance Speech Corrupted by Colored Noise