Convolutive non-negative matrix factorization (CNMF) and its sparse version, convolutive non-negative sparse coding (CNSC), exhibit great success in speech processing. A particular limitation of the current CNMF/CNSC approaches is that the convolution ranges of the bases in learning are identical, resulting in patterns covering the same time span. This is obvious unideal as most of sequential signals, for example speech, involve patterns with a multitude of time spans. This paper extends the CMNF/CNSC algorithm and presents a heterogeneous learning approach which can learn bases with non-uniformed convolution ranges. The validity of this extension is demonstrated with a simple speech separation task