An improved characteristic waveform decomposition based on nonnegative matrix factorization was proposed.Two methods based on Bayesian Ying-Yang(BYY)harmony learning and rival penalized competitive learning(RPCL)to compute factorization rank of nonnegative matrix factorization(NMF)were proposed.Computational complexity is decreased and speech quality is not decreased obviously.Mixed autoregressive model for construction of WI phase was proposed according to the energy of CW and coding matrix

which improves the naturalness.In the end

a low complexity NMF-WI speech coding at 2kb/s was developed.NMF based on Kullback-Leibler divergence and Mel scale band-partitioning initialization used for basis vectors were proposed

and CWs were classified into six based on pitch distribution.In CW factorization

computational complexity dropped by 10 MOPS.Speech quality is increased

and equivalent to 2.16kb/s NMF-WI using 4bit phase VQ.