As a cutting-edge technology in speech signal processing， speech separation has significant research value and broad application prospects. Typically， the signal captured by the microphones contains speech signals from multiple speakers， noise and reverberation. To improve the user experience and the performance of backend devices， it is necessary to perform speech separation. Speech separation originated from the well-known cocktail party problem. It aims to separate the speech signals from the mixed signal. In recent years， researchers have proposed a large number of speech separation methods， which have significantly improved separation performance. This paper systematically reviews and summarizes these methods. First， based on whether the auxiliary information of the target speaker is leveraged， speech separation is divided into two categories， i.e.， multi-speaker separation and target speaker extraction. Second， these methods are introduced in detail， following the progression from conventional approaches to deep learning-based techniques. Finally， the existing challenges in speech separation are discussed and prospective research in the future are highlighted.