The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Speech Time-Scale Modification With GANs
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“An alternative technique leveraged generative adversarial networks for TSM of speech signals [27], but its data-driven nature imposes limitations on its application to general audio.”
From Noise Morphing for Audio Time Stretching (2024) · cites this paper as [27]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
“Waveglow: a flow-based generative network for speech synthesis,”
Prenger, R., Valle, R., & Catanzaro, B. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8683143 · Cited by 1,004
Deep voice 3: scaling text-to-speech with convolutional sequence learning
Ping, W., Peng, K., Gibiansky, A., Arik, S. O., Kannan, A., Narang, S., Raiman, J., & Miller, J. (2017) · arXiv · doi:10.48550/arxiv.1710.07654 · Cited by 313
Improving Time-Scale Modification of Music Signals Using Harmonic-Percussive Separation
Driedger, J., Muller, M., & Ewert, S. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2013.2294023 · Cited by 64
Research works printed but not linked: 22
- 10.1109/ICASSP.1993.319366 · DOI · Paper page
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- Karrer, 2006, PhaVoRIT: A phase vocoder for real-time interactive time-stretching, 708
- 10.1109/ICASSP.2018.8461368 · DOI · Paper page
- Kong, 2020, HiFi-GAN: Generative adversarial networks for efficient and high fidelity speech synthesis, 33, 17022
- 10.1109/ICASSP40776.2020.9053795 · DOI
- Binkowski, 2020, High fidelity speech synthesis with adversarial networks
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- 10.1109/ICCV.2017.244 · DOI · Paper page
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- Pickett, 1980, The Sounds of Speech Communication
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Cited by
Noise Morphing for Audio Time Stretching
Moliner, E., Fierro, L., Wright, A., Hämäläinen, M. S., & Välimäki, V. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3386118 · Cited by 2
Extreme Audio Time Stretching Using Neural Synthesis
Fierro, L., Wright, A., Välimäki, V., & Hämäläinen, M. (2023) · ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp49357.2023.10094738 · Cited by 2
Metadata
- Authors
- Cohen, E., Kreuk, F., & Keshet, J.
- Venue
- IEEE Signal Processing Letters
- Published
- 2022
- Volume
- 29
- Pages
- 1067-1071
- ISSN
- 1070-9908
- 1558-2361
- Cited by
- 13
- References
- 6
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- CoChat. (2026). Citation check: Speech Time-Scale Modification With GANs. https://app.cochat.ai/papers/10.1109/lsp.2022.3164361
- The paper
- Cohen, E., Kreuk, F., & Keshet, J. (2022). Speech Time-Scale Modification With GANs. IEEE Signal Processing Letters. https://doi.org/10.1109/lsp.2022.3164361
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
