In this paper, we propose a speech enhancement algorithm by using MAP estimation with variable speech spectral amplitude probability density function (speech PDF). The variable speech PDF has two adaptive shape parameters which affect the quality of enhanced speech. Noise can be efficiently suppressed when these parameters are properly applied so that the variable speech PDF shape fits to the real-speech PDF one. We derive adaptive shape parameters from real-speech PDF in various narrow SNR intervals. The proposed speech enhancement algorithm with adaptive shape parameters is examined and compared to conventional algorithms. The simulation results show that the proposed method improved SegSNR around 6 and 9 dB when the input speech signal was corrupted by white and tunnel noises at 0 dB, respectively.

This paper proposes a wide-band noise reduction method using a zero phase (ZP) signal which is defined as the IDFT of a spectral amplitude. When a speech signal has periodicity in a short observation, the corresponding ZP signal becomes also periodic. On the other hand, when a noise spectral amplitude is approximately flat, its ZP signal takes nonzero values only around the origin. Hence, when a periodic speech signal is embedded in a flat spectral noise in an analysis frame, its ZP signal becomes a periodic signal except around the origin. In the proposed noise reduction method, we replace the ZP signal around the origin with the ZP signal in the second or latter period. Then, we get an estimated speech ZP signal. The major advantages of this method are that it can reduce not only stationary wide-band noises but also non-stationary wide-band noises and does not require a prior estimation of the noise spectral amplitude. Simulation results show that the proposed noise reduction method improves the SNR more than 5 dB for a tunnel noise and 13 dB for a clap noise in a low SNR environment.