Frequency Dependent Noise Flooring Parameter (FDNFP) for Speech Enhancement using Linear Prediction

Speech enhancement aims to improve speech quality by using various algorithms. It may sound simple, but at least it should have the terms clarity and intelligibility, pleasantness, or compatibility with some other method in speech processing. An enhancement method for single-channel speech degraded by additive noise is proposed. A spectral weighting functions derived by constrained optimization to suppress noise in the frequency domain. Two design parameters are included in the suppression gain, namely, the frequency-dependent noise flooring parameter (FDNFP) and the gain factor. The FDNFP controls the level of admissible residual noise in the enhanced speech. Enhanced harmonic structures are incorporated into the FDNFP by time-domain processing of the linear prediction residuals of voiced speech. Further enhancement of the harmonics is achieved by adaptive comb filtering derived using the gain factor with a peak-picking algorithm. Moto of this paper is to reduce the background noise added in the original speech signal so that listener will able to listen the noiseless speech along with clear voice. Here, we have implemented this paper for previously recorded historical speech signal. Advantage of this paper is that we can get clear voice from the noisy one and can store the speech for long time without destroying.