Real-time speech enhancement algorithm for transient noise suppression

Abstract

To effectively restrain stationary noise and transient noise, a real-time single-channel speech enhancement algorithm is proposed. First, to evaluate stationary noise, the quantile noise estimation method is used to obtain the spectrum of stationary noise. Then, based on the normalized variance and gravity center of the signal, the transient noise detection method is proposed to modify the spectrum of stationary noise. Next, the speech presence probability is estimated based on the speech features and harmonic analysis. Finally, the optimized-modified log-spectral amplitude (OM-LSA) estimator is adopted for speech enhancement. The experimental noise contains 115 environmental sounds with the SNR of −10 to 10\xa0dB. The experimental results show that the performance of the proposed algorithm is comparable to the OM-LSA algorithm which has good denoising performance, but the real-time performance of the former is much better. Compared with the Webrtc real-time algorithm, under the overall performance of stationary noise and transient noise, the overall speech quality indicators of the improved algorithm increased by 7.5%, 7.8% and 5.0%, respectively. And the short-time objective intelligibility increased by 2.4%, 2.4% and 2.0%, respectively. Even compared with the recurrent neural network(RNN) algorithm, the suppression performance of the transient noise is better. Besides, the real-time experiment base on the hardware platform shows that the runtime of processing a 10\xa0ms frame is 4.3\xa0ms.