Single-channel speech separation using combined EMD and speech-specific information

Multi-channel blind source separation (BSS) methods use more than one microphone. There is a need to develop speech separation algorithms under single microphone scenario. In this paper we propose a method for single channel speech separation (SCSS) by combining empirical mode decomposition (EMD) and speech specific information. Speech specific information is derived in the form of source-filter features. Source features are obtained using multi pitch information. Filter information is estimated using formant analysis. To track multi pitch information in the mixed signal we apply simple-inverse filtering tracking (SIFT) and histogram based pitch estimation to excitation source information. Formant estimation is done using linear predictive (LP) analysis. Pitch and formant estimation are done with and without EMD decomposition for better extraction of the individual speakers in the mixture. Combining EMD with speech specific information provides encouraging results for single-channel speech separation. © 2017, Springer Science+Business Media, LLC.