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Signal estimation from modified short-time Fourier transform
Abstract
In this paper, we present an algorithm to estimate a signal from its modified short-time Fourier transform (STFT). This algorithm is computationally simple and is obtained by minimizing the mean squared error between the STFT of the estimated signal and the modified STFT. Using this algorithm, we also develop an iterative algorithm to estimate a signal from its modified STFT magnitude. The iterative algorithm is shown to decrease, in each iteration, the mean squared error between the STFT magnitude of the estimated signal and the modified STFT magnitude. The major computation involved in the iterative algorithm is the discrete Fourier transform (DFT) computation, and the algorithm appears to be real-time implementable with current hardware technology. The algorithm developed in this paper has been applied to the time-scale modification of speech. The resulting system generates very high-quality speech, and appears to be better in performance than any existing method.
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“It can be shown that, when computing the synthesis frames by Equation ( 18), the STFT of y (when choosing H a = H s ) minimizes a squared error distance measure defined in [16].”
From A Review of Time-Scale Modification of Music Signals (2016) · cites this paper as [16]
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Cited by
Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions
Shen, J., Pang, R., Weiss, R. J., Schuster, M., Jaitly, N., Yang, Z., Chen, Z., Zhang, Y., Wang, Y., Skerrv-Ryan, R., et al. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8461368 · Cited by 2,791
Tacotron: towards end-to-end speech synthesis
Wang, Y., Skerry-Ryan, R. J., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., Yang, Z., Xiao, Y., Chen, Z., Bengio, S., et al. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-1452 · Cited by 1,746
“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
Neural Speech Synthesis with Transformer Network
Li, N., Liu, S., Liu, Y., Zhao, S., & Liu, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016706 · Cited by 754
ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection
Todisco, M., Wang, X., Vestman, V., Sahidullah, M., Delgado, H., Nautsch, A., Yamagishi, J., Evans, N., Kinnunen, T. H., & Lee, K. A. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-2249 · Cited by 687
Metadata
- Authors
- Griffin, D., & Lim, J.
- Venue
- IEEE Transactions on Acoustics, Speech, and Signal Processing
- Published
- 1984-04
- Volume
- 32
- Issue
- 2
- Pages
- 236-243
- ISSN
- 0096-3518 (ISSN-L)
- OpenAlex
- W2120847449
- Cited by
- 2,095
- References
- 0
- FWCI
- 5.29
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Signal estimation from modified short-time Fourier transform. https://app.cochat.ai/papers/10.1109/tassp.1984.1164317
- The paper
- Griffin, D., & Lim, J. (1984). Signal estimation from modified short-time Fourier transform. IEEE Transactions on Acoustics, Speech, and Signal Processing. https://doi.org/10.1109/tassp.1984.1164317
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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