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Multi-Domain Processing via Hybrid Denoising (MDPhD) Networks for Speech Enhancement

Jang-Hyun Kim^* (Seoul National University, Works done at Clova AI Research), Jaejun Yoo^* (Clova AI Research), Sanghyuk Chun (Clova AI Research), Adrian Kim (Clova AI Research), Jung-Woo Ha (Clova AI Research)

*indicates equal contribution

[paper (arXiv)]

Evaluation Results

	CSIG	CBAK	COVL	PESQ	SSNR
Wiener ¹	3.23	2.68	2.67	2.22	5.07
SEGAN ²	3.48	2.94	2.80	2.16	7.73
Wavenet ³	3.62	3.23	2.98	-	-
MMSE-GAN ⁴	3.80	3.12	3.14	2.53	-
MDPhD (ours)	3.85	3.39	3.27	2.70	10.22

Audio Samples

	Sample 1	Sample 2	Sample 3	Sample 4	Sample 5
Noisy input
SEGAN
Wavenet
Ours

Citation

@article{kim2018mdphd,
    title={Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement},
    author={Kim, Jang-Hyun and Yoo, Jaejun and Chun, Sanghyuk and Kim, Adrian and Ha, Jung-Woo},
    journal={arXiv preprint arXiv:1812.08914},
    year={2018}
}

References

Pascal Scalart and Jozue Vieira Filho. Speech enhancement based on a priori signal to noise estimation. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1996
Santiago Pascual, Antonio Bonafonte, and Joan Serra. SEGAN: Speech enhancement generative adversarial network. In Interspeech, 2017.
Dario Rethage, Jordi Pons, and Xavier Serra. A Wavenet for speech denoising. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
Meet H Soni, Neil Shah, and Hemant A Patil. Time-frequency masking-based speech enhancement using generative adversarial network. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.

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