This paper presents a novel data adaptive thresholding approach to single channel speech enhancement. The noisy speech signal and fractional Gaussian noise (fGn) are combined to produce the complex signal. The fGn is generated using the noise variance roughly estimated from the noisy speech signal. Bivariate empirical mode decomposition (bEMD) is employed to decompose the complex signal into a finite number of complex-valued intrinsic mode functions (IMFs). The real and imaginary parts of the IMFs represent the IMFs of observed speech and fGn, respectively. Each IMF is divided into short time frames for local processing. The variance of IMF of fGn calculated within a frame is used as the reference term to classify corresponding noisy speech frame into noise and signal dominant frames. Only the noise dominant frames are soft-thresholded to reduce the noise effects. Then, all the frames as well as IMFs of speech are combined, yielding the enhanced speech signal. The experimental results show the improved performance of the proposed algorithm compared to the recently reported methods. 1. Introduction The research on speech enhancement is motivated by the rapidly growing market of speech communication applications, such as teleconferencing, hands-free telephony, hearing-aids, and speech recognition. In hands-free communication systems, the microphone(s) is typically placed at a certain distance from the speaker. In adverse acoustic environment, various noise sources make the speech signal corrupted. Although, the human auditory system is remarkably robust in most adverse situations, noise effects heavily affect the performance of automatic speech recognition (ASR) systems. The performance of an ASR system trained in one specific environment will drop considerably when used in another acoustic environment [1]. Several approaches have already been proposed to improve the speech enhancement results. Although the microphone array based approach exhibits better results, at the same time speech processing research community is trying to reduce the number of microphones (channels). The spectral subtraction is one of the early methods to reduce the noise effects from the observed speech signals. In this method, the noise reduction is achieved by appropriate adjustment of the set of spectral magnitudes [2]. Its basic requirement is the noise spectrum which is determined from the nonspeech segments [3]. In such single channel speech enhancement system, the residual noise is a usual issue. It decreases the speech intelligibility and hence further processing is
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