Nonnegative matrix partial co-factorization (NMPCF) is a joint matrix decomposition algorithm integrating prior knowledge of specific source to help separate specific source signal from monaural mixtures. Convolutive nonnegative matrix factorization (CNMF), which introduces the concept of a convolutive non-negative basis set during NMF process, opens up an interesting avenue of research in the field of monaural sound separation. On the basis of the above two algorithms, we propose a speech separation algorithm named as convolutive nonnegative matrix partial co-factorization (CNMPCF) for low signal noise ratio (SNR) monaural speech. Firstly, through a voice detection process exploring fundamental frequency estimation algorithm, we divide a mixture signal into vocal and nonvocal parts, thus those vocal parts are used as test mixture signal while the nonvocal parts (pure noise) participat in the partial joint decomposition. After CNMPCF, we can obtain the separated speech spectrogram. Then, the separated speech signal can reconstructed through Inverse short time fourier transformation. In the experiments, we select 5 SNRs from 0 dB to -12 dB at -3 dB intervals to obtain low SNR mixture speeches. The results demonstrate that the proposed CNMPCF approach has superiority over sparse convolutive nonnegative matrix factorization (SCNMF) and NMPCF under different noise types and noise intensities. Copyright © 2020 Acta Automatica Sinica. All rights reserved.
