Computational Auditory Scene Analysis (CASA) has been the focus in recent literature for speech separation from monaural mixtures. The performance of current CASA systems on voiced speech separation strictly depends on the robustness of the algorithm used for pitch frequency estimation. We propose a new system that estimates pitch (frequency) range of a target utterance and separates voiced portions of target speech. The algorithm, first, estimates the pitch range of target speech in each frame of data in the modulation frequency domain, and then, uses the estimated pitch range for segregating the target speech. The method of pitch range estimation is based on an onset and offset algorithm. Speech separation is performed by filtering the mixture signal with a mask extracted from the modulation spectrogram. A systematic evaluation shows that the proposed system extracts the majority of target speech signal with minimal interference and outperforms previous systems in both pitch extraction and voiced speech separation.
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