Speech Denoising with Deep Feature Losses

Speech Denoising with Deep Feature Losses (arXiv, Github page)

François G. Germain, Qifeng Chen and Vladlen Koltun

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Algorithms:
- Noisy: Input speech file degraded by background noise.
- Our approach: Speech file processed with our fully convolutional context aggregation stack trained with a deep feature loss.
- Wiener: Speech file processed with Wiener filtering with a priori signal-to-noise ratio estimation (Hu and Loizou, 2006).
- Wavenet: Speech file processed with the Wavenet-like speech enhancement deep network (Rethage et al., 2018).
- SEGAN: Speech file processed with the SEGAN speech enhancement deep network (Pascual et al., 2017).

=> Select BAK tranche:

Tranche 1	Tranche 2	Tranche 3	Tranche 4	Tranche 5	Tranche 6	Tranche 7	Tranche 8
(Hardest/Noisiest)		============================>				(Easiest/Cleanest)

Audio samples from tranche 7

Filename	Noisy	Our approach	Wiener	Wavenet	SEGAN
p232_299.wav
p232_101.wav
p257_284.wav
p232_346.wav
p232_086.wav

Data source: Test dataset from University of Edinburgh noisy dataset (Valentini-Botinhao et al., 2016).

References

Y. Hu and P. C. Loizou, "Subjective comparison of speech enhancement algorithms," in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2006.
S. Pascual, A. Bonafonte, and J. Serrà, "SEGAN: Speech enhancement generative adversarial network," in Interspeech, 2017.
D. Rethage, J. Pons, and X. Serra, "A wavenet for speech denoising," in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018.
C. Valentini-Botinhao, X. Wang, S. Takaki, and J. Yamagishi, "Investigating RNN-based speech enhancement methods for noise-robust text-to-speech," in ISCA Speech Synthesis Workshop, 2016.