The DOI matches its DataCite record.
Towards objective evaluation of audio time-scale modification methods
Abstract
The need for high-quality time-scale modification of audio is increasing, as media streaming services are providing new related functionalities to their users. The main goal of a time-stretching method is to preserve the pitch and the subjective quality of the different components of the audio signal, namely transients, noise, and tonal components. Many solutions have been proposed throughout the years, with various results depending on the kind of processed audio input. This paper introduces an evaluation method for audio time-scaling algorithms based on a recent fuzzy time-frequency decomposition, which reveals the energy of the tonal, transient, and noise components in the original and stretched sounds. From the energy curves, typical impairments, such as transient smearing and the loss of tonality, can be observed. This analysis approach is compared with the subjective preferences of different techniques. This leads to suggestions for possible improvements of future algorithms. The ultimate goal is having an objective evaluation method which matches the subjective quality assessment.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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
“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 [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Self-citation
Metadata
- Authors
- Fierro, L., & Välimäki, V.
- Venue
- Zenodo
- Publisher
- Zenodo
- Published
- 2020-06-17
- References
- 0
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.
