Abstract
Deep learning-based speech enhancement algorithms have shown their powerful ability in removing both stationary and non-stationary noise components from noisy speech observations. But they often introduce artificial residual noise, especially when the training target does not contain the phase information, e.g., ideal ratio mask, or the clean speech magnitude and its variations. It is well-known that once the power of the residual noise components exceeds the noise masking threshold of the human auditory system, the perceptual speech quality may degrade. One intuitive way is to further suppress the residual noise components by a postprocessing scheme. However, the highly non-stationary nature of this kind of residual noise makes the noise power spectral density (PSD) estimation a challenging problem. To solve this problem, the paper proposes three strategies to estimate the noise PSD frame by frame, and then the residual noise can be removed effectively by applying a gain function based on the decision-directed approach. The objective measurement results show that the proposed postfiltering strategies outperform the conventional postfilter in terms of segmental signal-to-noise ratio (SNR) as well as speech quality improvement. Moreover, the AB subjective listening test shows that the preference percentages of the proposed strategies are over 60%.
Highlights
In the last decade, the huge success of deep learning has been witnessed in the field of speech enhancement
Typical deep neural networks (DNNs) contain fully connected networks (FCNs) [1], recurrent neural networks (RNNs), e.g., networks consist of long short-term memory (LSTM) layers [2,3,4], and convolutional neural networks (CNNs) [5,6,7]
2.2.2 Results and analysis Speech spectrograms before and after DNN-based speech enhancement processing are presented in Fig. 1, where the speech was randomly chosen from the test set, and the background noise was white Gaussian noise with signal-to-noise ratio (SNR) = 0 dB
Summary
The huge success of deep learning has been witnessed in the field of speech enhancement. Among all the CNNs, the the typical deep learning-based speech enhancement methods often introduce artificial residual noise, especially when the phase information is neglected in the training target [12], e.g., ideal ratio mask [13, 14], or the magnitude of the clean speech and its variations [10, 11, 15]. This kind of noise is highly non-stationary, and its power remains considerable in the middle-high frequency band where the speech power spectral density (PSD) is relatively low. According to the human hearing model which is widely used in wideband audio coding [16,17,18,19], when the residual noise PSD exceeds the noise
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
