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: Efficient Alternatives to the Ephraim and Malah Suppression Rule for Audio Signal Enhancement | EURASIP Journal onApplied Signal Processing 2003 10 1043-1051 2003 Hindawi Publishing Corporation Efficient Alternatives to the Ephraim and Malah Suppression Rule for Audio Signal Enhancement Patrick J. Wolfe Signal Processing Group Department of Engineering University of Cambridge CB2 1PZ Cambridge UK Email pjw47@ Simon J. Godsill Signal Processing Group Department of Engineering University of Cambridge CB2 1PZ Cambridge UK Email sjg@ Received 31 May 2002 and in revised form 20 February 2003 Audio signal enhancement often involves the application of a time-varying filter or suppression rule to the frequency-domain transform of a corrupted signal. Here we address suppression rules derived under a Gaussian model and interpret them as spectral estimators in a Bayesian statistical framework. With regard to the optimal spectral amplitude estimator of Ephraim and Malah we show that under the same modelling assumptions alternative methods of Bayesian estimation lead to much simpler suppression rules exhibiting similarly effective behaviour. We derive three of such rules and demonstrate that in addition to permitting a more straightforward implementation they yield a more intuitive interpretation of the Ephraim and Malah solution. Keywords and phrases noise reduction speech enhancement Bayesian estimation. 1. INTRODUCTION Herein we address an important issue in audio signal processing for multimedia communications that of broadband noise reduction for audio signals via statistical modelling of their spectral components. Due to its ubiquity in applications of this nature we concentrate on short-time spectral attenuation a popular method of broadband noise reduction in which a time-varying filter or suppression rule is applied to the frequency-domain transform of a corrupted signal. We first address existing suppression rules derived under a Gaussian statistical model and interpret them in a Bayesian framework. We then employ the same model and .
