Improvements on co-channel speech separation using ADF: low complexity, fast convergence, and generalization

Three modifications on the adaptive decorrelation filtering (ADF) algorithm are proposed to improve the performance of a co-channel speech separation system. Firstly, a simplified ADF (SADF) is suggested to reduce the computational complexity of ADF from O(N²) to O(N) per sample, where N is the filter length used in the channel estimation. Secondly, a transform-domain ADF (TDADF) is developed to accelerate the convergence of the filter estimates while maintaining computational complexity at O(N). Thirdly, a generalized ADF (GADF) is derived to handle the noncausal filter estimation problem often encountered in co-channel speech separation. Experimental results showed that when the average signal-to-interference ratios (SIRs) in the co-channel signals were 6.15 and 5.38 dB, respectively, both the SADF and TDADF improved the SIRs to around 18 to 19 dB, and the GADF further improved the SIRs to around 19 to 24 dB