The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network
Open accessAbstract
Time-frequency (T-F) domain masking is a mainstream approach for single-channel speech enhancement. Recently, focuses have been put to phase prediction in addition to amplitude prediction. In this paper, we propose a phase-and-harmonics-aware deep neural network (DNN), named PHASEN, for this task. Unlike previous methods which directly use a complex ideal ratio mask to supervise the DNN learning, we design a two-stream network, where amplitude stream and phase stream are dedicated to amplitude and phase prediction. We discover that the two streams should communicate with each other, and this is crucial to phase prediction. In addition, we propose frequency transformation blocks to catch long-range correlations along the frequency axis. Visualization shows that the learned transformation matrix implicitly captures the harmonic correlation, which has been proven to be helpful for T-F spectrogram reconstruction. With these two innovations, PHASEN acquires the ability to handle detailed phase patterns and to utilize harmonic patterns, getting 1.76dB SDR improvement on AVSpeech + AudioSet dataset. It also achieves significant gains over Google's network on this dataset. On Voice Bank + DEMAND dataset, PHASEN outperforms previous methods by a large margin on four metrics.
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Cites
Audio Set: An ontology and human-labeled dataset for audio events
Gemmeke, J. F., Ellis, D. P. W., Freedman, D., Jansen, A., Lawrence, W., Moore, R. C., Plakal, M., & Ritter, M. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7952261 · Cited by 3,134
Performance measurement in blind audio source separation
Vincent, E., Gribonval, R., & Fevotte, C. (2006) · IEEE Transactions on Audio, Speech and Language Processing · doi:10.1109/tsa.2005.858005 · Cited by 2,889
Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation
Luo, Y., & Mesgarani, N. (2019) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2019.2915167 · Cited by 2,042
Evaluation of Objective Quality Measures for Speech Enhancement
Hu, Y., & Loizou, P. C. (2008) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2007.911054 · Cited by 1,830
On Training Targets for Supervised Speech Separation
Wang, Y., Narayanan, A., & Wang, D. (2014) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2014.2352935 · Cited by 1,067
Complex Ratio Masking for Monaural Speech Separation
Williamson, D. S., Wang, Y., & Wang, D. (2016) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2015.2512042 · Cited by 797
Phase-sensitive and recognition-boosted speech separation using deep recurrent neural networks
Erdogan, H., Hershey, J. R., Watanabe, S., & Le Roux, J. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178061 · Cited by 646
Speech Enhancement with LSTM Recurrent Neural Networks and its Application to Noise-Robust ASR
Weninger, F., Erdogan, H., Watanabe, S., Vincent, E., Le Roux, J., Hershey, J. R., & Schuller, B. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-22482-4_11 · Cited by 602
Ideal ratio mask estimation using deep neural networks for robust speech recognition
Narayanan, A., & Wang, D. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639038 · Cited by 526
Investigating RNN-based speech enhancement methods for noise-robust Text-to-Speech
Valentini-Botinhao, C., Wang, X., Takaki, S., & Yamagishi, J. (2016) · 9th ISCA Workshop on Speech Synthesis Workshop (SSW 9) · doi:10.21437/ssw.2016-24 · Cited by 463
Cited by
Reddy, C. K. A., Gopal, V., Cutler, R., Beyrami, E., Cheng, R., Dubey, H., Matusevych, S., Aichner, R., Aazami, A., Braun, S., et al. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-3038 · Cited by 363
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
Michelsanti, D., Tan, Z.-H., Zhang, S.-X., Xu, Y., Yu, M., Yu, D., & Jensen, J. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2021.3066303 · Cited by 316
Metadata
- Authors
- Yin, D., Luo, C., Xiong, Z., & Zeng, W.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2020-04-03
- Volume
- 34
- Issue
- 05
- Pages
- 9458-9465
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2998161426
- Cited by
- 323
- References
- 23
- FWCI
- 27.72
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Yin, D., Luo, C., Xiong, Z., & Zeng, W. (2020). PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v34i05.6489
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