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Deep Learning-Based Single-Ended Quality Prediction for Time-Scale Modified Audio
Overview
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Claims the paper makes
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Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [10]
In the citing paper
“The weakness of established TSM methods, often based on a phase vocoder structure, lies in the poor description and scaling of the transient and noise components, or nuances, of a sound.”
From Extreme Audio Time Stretching Using Neural Synthesis (2023) · cites this paper as [10]
In the citing paper
“The subjective nature of audio time stretching further complicates the problem, as there is no clear objective metric for evaluation [9], [11].”
From Noise Morphing for Audio Time Stretching (2024) · cites this paper as [9]
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Citations
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
- Roberts, T., Nicolson, A., & Paliwal, K. K.
- Venue
- Journal of the Audio Engineering Society
- Publisher
- Audio Engineering Society
- Published
- 2021-09-10
- Volume
- 69
- Issue
- 9
- Pages
- 644-655
- ISSN
- 1549-4950
- Cited by
- 2
- References
- 0
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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.
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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: Deep Learning-Based Single-Ended Quality Prediction for Time-Scale Modified Audio. https://app.cochat.ai/papers/10.17743/jaes.2021.0031
- The paper
- Roberts, T., Nicolson, A., & Paliwal, K. K. (2021). Deep Learning-Based Single-Ended Quality Prediction for Time-Scale Modified Audio. Journal of the Audio Engineering Society. https://doi.org/10.17743/jaes.2021.0031
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.
