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Extending Spectral Modeling Synthesis with Transient Modeling Synthesis
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“A conceptually similar strategy for TSM has been proposed by Verma and Meng in [24] where they use a sines+transients+noise signal model [25][26][27][28] to parameterize a given signal's sinusoidal, transient, and noise-like sound components.”
From A Review of Time-Scale Modification of Music Signals (2016) · cites this paper as [27]
In the citing paper
“Audio signals can be considered to consist of sinusoidal, noise, and transient components [11][12][13][14].”
From Audio Time Stretching Using Fuzzy Classification of Spectral Bins (2017) · cites this paper as [14]
In the citing paper
“However, in contrast to methods where transients are explicitly separated from the input audio [13,14,28,34], the proposed method is more likely to keep transients perceptually intact with other components of the sound.”
From Audio Time Stretching Using Fuzzy Classification of Spectral Bins (2017) · cites this paper as [14]
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No references could be resolved yet.
Research works printed but not linked: 8
- George E. B., 1992, Journal of the Audio Engineering Society, 40, 497
- 10.1109/89.622558 · DOI
- International Standards Organization/Moving Pictures Expert Group, 1993, ISO/IEC, 11172
- 10.1109/TASSP.1986.1164910 · DOI · Paper page
- Rodet X., 1992, Proceedings of the International Computer Music Conference. San Francisco: International Computer Music Association, 410
- 10.2307/3680788 · DOI · Paper page
- Solbach L, 1996, Proceedings of the International Computer Music Conference. San Francisco: International Computer Music Association, 324
- Verma T., 1997, Proceedings of the International Computer Music Conference. San Francisco: International Computer Music Association, 164
Cited by
A Review of Time-Scale Modification of Music Signals
Driedger, J., & Müller, M. (2016) · Applied Sciences · doi:10.3390/app6020057 · Cited by 71
Audio Time Stretching Using Fuzzy Classification of Spectral Bins
Damskägg, E.-P., & Välimäki, V. (2017) · Applied Sciences · doi:10.3390/app7121293 · Cited by 34
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
- Verma, T. S., & Meng, T. H. Y.
- Affiliations
- Helsinki University of Technology, Laboratory of Acoustics and Audio Signal Processing, 02150 Espoo, Finland,
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA,
- Venue
- Computer Music Journal
- Publisher
- MIT Press
- Published
- 2000-06
- Volume
- 24
- Issue
- 2
- Pages
- 47-59
- ISSN
- 0148-9267
- 1531-5169
- Cited by
- 35
- References
- 0
- Language
- en
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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.
