Supervised Speech Enhancement with Real Spectrum Approximation

Speech enhancement aims to separate a target speech from background noise. Recently, speech enhancement has been formulated as a supervised learning problem, in which a learning machine is trained to estimate the target spectrum denoted as mapping-based method or a time-frequency mask denoted as masking-based method. Signal approximation methods indirectly estimate the target spectrum via the mask estimation, which combines the advantages of both mapping based and masking based methods. Moreover, conventional methods usually ignore the phase which is also important to the speech quality. To consider the phase, the complex number spectrum needs to be modeled. However, modeling may be difficult. In this work, a pure real number spectrum is used as an alternative representation of the complex number spectrum, and a signal approximation method is used for speech enhancement. Experimental results show that the proposed method outperforms other commonly used methods.