Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Speech Enhancement by MAP Spectral Amplitude Estimation Using a Super-Gaussian Speech Model Thomas Lotter | EURASIP Journal on Applied Signal Processing 2005 7 1110-1126 2005 T. Lotter and P. Vary Speech Enhancement by MAP Spectral Amplitude Estimation Using a Super-Gaussian Speech Model Thomas Lotter Institute of Communication Systems and Data Processing RWTH Aachen University of Technology RWTH Aachen 52056 Aachen Germany Siemens Audiological Engineering Group Gebbertstrasse 125 91058 Erlangen Germany Email Peter Vary Institute of Communication Systems and Data Processing RWTH Aachen University of Technology RWTH Aachen 52056 Aachen Germany Email vary@ Received 7 June 2004 Revised 17 September 2004 Recommended for Publication by Jacob Benesty This contribution presents two spectral amplitude estimators for acoustical background noise suppression based on maximum a posteriori estimation and super-Gaussian statistical modelling of the speech DFT amplitudes. The probability density function of the speech spectral amplitude is modelled with a simple parametric function which allows a high approximation accuracy for Laplace- or Gamma-distributed real and imaginary parts of the speech DFT coefficients. Also the statistical model can be adapted to optimally fit the distribution of the speech spectral amplitudes for a specific noise reduction system. Based on the superGaussian statistical model computationally efficient maximum a posteriori speech estimators are derived which outperform the commonly applied Ephraim-Malah algorithm. Keywords and phrases speech enhancement MAP estimation speech model. 1. INTRODUCTION The reduction of acoustical background noise using a single microphone is an important subject to improve the quality of speech communication systems in the context of digital hearing aids speech recognition hands-free telephony or teleconferencing. Although single-microphone speech enhancement has been a research topic for decades the estimation of a clean speech signal from its noisy observation remains a challenging
