Instant Noise Estimation Using Fourier Transform of AMDF and Variable Start Minima Search
Lin Zhong, Rafik A Goubran · 2006
The paper proposes a new algorithm to estimate and suppress highly non-stationary background noise from speech. The algorithm consists of two spectral detectors. The first one uses strict criteria and is based on the Fourier transform of the AMDF (average magnitude difference function). The second one uses loose criteria and is based on variable start minima search. By combining the two detectors, the algorithm instantaneously detects and tracks a sudden change of noise energy level. The proposed algorithm is then merged to conventional MMSE-STSA (MMSE short time spectral amplitude) to suppress non-stationary noise in speech. Simulation results are given to show the superiority of our proposed algorithm.