The impact of exploiting spectro-temporal context in computational speech segregation

DOI10.5281/zenodo.1202197Zenodo1202197MaRDI QIDQ6698418

Dataset published at Zenodo repository.

Thomas Bentsen, Abigail Anne Kressner, Tobias May, Torsten Dau

Publication date: 1 January 2018

Copyright license: Creative Commons Attribution 4.0 International

Description

The experimental datafrom the study: https://asa.scitation.org/doi/10.1121/1.5020273 Group 1 contains results, masks and audio from the models of the 16 GMM component segregation system Group 2 contains results, masks and audio from the models of the 64 GMM component segregation system There are three folders: Audio: The CLUE sentences that were used for the listener study IBM = Ideal Binary Mask, UP = UnProcessed, EBM = Estimated Binary Mask. The IBM and UP are stored in one of the configuration folders (Front-end), that is: Audio\Group1\Front-end\icra_01_10sec_matched\UP Audio\Group1\Front-end\icra_01_10sec_matched\IBM Audio\Group1\Front-end\icra_01_10sec_matched\EBM Results: The computed metrics for group 1 2 as well as Word Recognition Scores (WRSs) from the listener study BinaryMasks: a priori SNR masks, IBMs and EBMs from group 1 and 2. Developed with Matlab R2016a.

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