Improved Normalizing Flow-Based Speech Enhancement using an All-pole Gammatone Filterbank for Conditional Input Representation
Martin Strauss, Matteo Torcoli and Bernd Edler
presented at SLT, 2022
Examples
Listening test items. The test data is the dataset published by Valentini et al. [1]. The comparing methods are MetricGAN+ [2] and CDiffuSE [3].
| ID | Gender | Noise | Test input SNR [dB] | Website |
|---|---|---|---|---|
| p232_003 | male | Bus | 7.5 | Link |
| p232_219 | male | Cafe | 7.5 | Link |
| p232_005 | male | Bus | 2.5 | Link |
| p232_032 | male | Cafe | 2.5 | Link |
| p257_186 | female | Bus | 7.5 | Link |
| p257_049 | female | Cafe | 7.5 | Link |
| p257_367 | female | Bus | 2.5 | Link |
| p257_251 | female | Cafe | 2.5 | Link |
References
[1] C. Valentini-Botinhao, X. Wang, S. Takaki, and J. Yamagishi, “Speech enhancement for a noise-robust text-to-speech synthesis system using deep recurrent neural networks,” in Proceedings Interspeech Conference, 2016, pp. 352–356.
[2] S.-W. Fu, C. Yu, T.-A. Hsieh, P. Plantinga, M. Ravanelli, X. Lu, Y. Tsao, "MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement", in Proceedings Interspeech Conference, 2021, pp. 201–205.
[3] Y.-J. Lu, Z.-Q. Wang, S. Watanabe, A. Richard, C. Yu, and Y. Tsao, "CONDITIONAL DIFFUSION PROBABILISTIC MODEL FOR SPEECH ENHANCEMENT", in Proceedings ICASSP, 2022, pp. 7402-7406