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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Learning Optimum Number of Bases for Indian Languages in Non-negative Matrix Factorization based Multilingual Speech Separation

Author 1: Nandini C Nag
Author 2: Milind S Shah

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2021.0120809

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 8, 2021.

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Abstract: Non-negative matrix factorization-based audio source separation separating a target source has shown significant performance improvement when the spectral bases attained after factorization exhibits latent structures in the mixed audio signal comprising multiple speaker sources. If all the sources are known, the spectral bases may be inferred on priority by using a training process on the database of isolated sources. The number of bases inferred for a source should not include bases matching spectral patterns of the interfering sources in the audio mixture; otherwise, the estimated target source after separation will be incorporated with undesirable spectral patterns. It is difficult to distinguish and separate similar audio sources in an overlapped speech, leading to a complex speech processing task. Therefore, this research attempts to learn an optimum number of bases for Indian languages leading to successful separation of target source in multi-lingual multiple speaker speech mixtures using non-negative matrix factorization. The languages used for utterances are Hindi, Marathi, Gujarati, and Bengali. The speaker combinations used are female-female, male-male, and female-male. The optimum number of bases which was determined by evaluating improvement in the separation performance was found to be 40 for all the languages considered.

Keywords: Indian languages; optimum number of bases; non-negative matrix factorization; speech separation

Nandini C Nag and Milind S Shah, “Learning Optimum Number of Bases for Indian Languages in Non-negative Matrix Factorization based Multilingual Speech Separation” International Journal of Advanced Computer Science and Applications(IJACSA), 12(8), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120809

@article{Nag2021,
title = {Learning Optimum Number of Bases for Indian Languages in Non-negative Matrix Factorization based Multilingual Speech Separation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120809},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120809},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {8},
author = {Nandini C Nag and Milind S Shah}
}


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