The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Phase-vocoder: about this phasiness business
Overview
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Claims other papers make about this one
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In the citing paper
“Traditional TSM artifacts such as transient smearing, loss of presence, and phasiness are heavily accentuated and cause poor audio quality when the TSM factor is four or larger.”
From Extreme Audio Time Stretching Using Neural Synthesis (2023) · cites this paper as [11]
In the citing paper
“This task becomes especially challenging with large stretching factors, where conventional methods, such as the phase vocoder, often introduce perceptual artifacts, e.g., transient smearing, loss of presence, and phasiness [3], [4], [10].”
From Noise Morphing for Audio Time Stretching (2024) · cites this paper as [10]
In the citing paper
“Even with these kind of signals however, the phase vocoder TSM introduces an artifact typically described as “phasiness” to the processed sound [21,22].”
From Audio Time Stretching Using Fuzzy Classification of Spectral Bins (2017) · cites this paper as [22]
Self-check
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Citations
Cites
Improved phase vocoder time-scale modification of audio
Laroche, J., & Dolson, M. (1999) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.759041 · Cited by 276
Self-citation
Research works printed but not linked: 4
- 10.1109/ASPAA.1995.482995 · DOI
- 10.1109/TASSP.1984.1164317 · DOI · Paper page
- 10.2307/3680093 · DOI
- 10.1016/0167-6393(94)00054-E · DOI
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
A time-scale modification dataset with subjective quality labels
Roberts, T., & Paliwal, K. K. (2020) · The Journal of the Acoustical Society of America · doi:10.1121/10.0001567 · Cited by 4
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
- Laroche, J., & Dolson, M.
- Venue
- Proceedings of 1997 Workshop on Applications of Signal Processing to Audio and Acoustics
- Publisher
- IEEE
- Published
- 2002
- Pages
- 4
- Cited by
- 23
- References
- 1
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Phase-vocoder: about this phasiness business. https://app.cochat.ai/papers/10.1109/aspaa.1997.625603
- The paper
- Laroche, J., & Dolson, M. (2002). Phase-vocoder: about this phasiness business. Proceedings of 1997 Workshop on Applications of Signal Processing to Audio and Acoustics. https://doi.org/10.1109/aspaa.1997.625603
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.
