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What is Supervised Speech Separation Targets about?

This paper evaluates various training targets for supervised speech separation, focusing on monaural systems using deep neural networks. It compares the effectiveness of the ideal binary mask (IBM), target binary mask (TBM), ideal ratio mask (IRM), short-time Fourier transform spectral magnitude (FFT-MAG), and Gammatone frequency power spectrum (GF-POW) in improving speech intelligibility and quality. Results indicate that the IRM and FFT-MASK outperform other targets, particularly in low signal-to-noise ra

Author

vortexdiniz

Language

EN