Noise Estimation and Suppression Using Nonlinear Function withA PrioriSpeech Absence Probability in Speech Enhancement

Abstract

This paper proposes a noise-biased compensation of minimum statistics (MS) method using a nonlinear function anda priorispeech absence probability (SAP) for speech enhancement in highly nonstationary noisy environments. The MS method is a well-known technique for noise power estimation in nonstationary noisy environments; however, it tends to bias noise estimation below that of the true noise level. The proposed method is combined with an adaptive parameter based on a sigmoid function anda prioriSAP for residual noise reduction. Additionally, our method uses an autoparameter to control the trade-off between speech distortion and residual noise. We evaluate the estimation of noise power in highly nonstationary and varying noise environments. The improvement can be confirmed in terms of signal-to-noise ratio (SNR) and the Itakura-Saito Distortion Measure (ISDM).

Hard component detection of transient noise and its removal using empirical mode decomposition and wavelet‐based predictive filter

“…where the index for the dominant component window is given by (19); therefore, the spatial correlation matrix for (i, ℓ)th window of length L is approximately estimated as…”

“…The short-time Fourier transform (STFT)-based method [14] and wavelet-based filtering methods [15][16][17] have also been used by the researchers for transient removal. Most of the filtering methods follow the general procedure as the noise signal decomposition [14,[18][19][20] and then the noise reduction. WS reductions from the IS also come under these categories of transient noise reduction.…”