Blind speech intelligiblility enhancement by a new dual modified predator-prey particle swarm optimization algorithm

This paper addresses the problem of acoustic noise cancellation by adaptive filtering algorithms. To deal with acoustic noise reduction and speech enhancement problems, we propose to use the modified predator-prey particle swarm optimization (MPPPSO) to design a new dual adaptive noise canceller based on swarm intelligence heuristic search. The proposed dual MPPPSO algorithm improves the single-channel PPPSO algorithm convergence speed behavior when a large filter length is used. Also, the proposed algorithm leads to a low steady-state error in comparison with the single-channel PPPSO algorithm behavior which fails with large filters length and non-stationary input. The proposed dual MPPPSO algorithm shows significant improvement in the system mismatch (SM) and Output signal-to-noise ratio (SNR) values. We present the simulation results of the proposed dual MPPPSO algorithm that confirm its superiority and good performances in comparison with the single-channel PPPSO and the two-channel normalized least mean square (2C-FNLMS) algorithm.