Time Scale Modification (TSM) is a well-researched field; however, no effective objective measure of quality exists.  This paper details the creation, subjective evaluation, and analysis of a dataset for use in the development of an objective measure of quality for TSM. Comprised of two parts, the training component contains 88 source files processed using six TSM methods at 10 time scales, while the testing component contains 20 source files processed using three additional methods at four time scales.

DOI Link: 
10.21227/ny9p-rv41
Links: 
MATLAB Algorithm Implementations
Griffith University Signal Processing Laboratory
arXiv Preprint
DOI
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[1] Timothy Roberts, "A Time-Scale Modification Dataset with Subjective Quality Labels", IEEE Dataport, 2020. [Online]. Available: http://dx.doi.org/10.21227/ny9p-rv41. Accessed: Nov. 18, 2024.
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doi = {10.21227/ny9p-rv41},
url = {http://dx.doi.org/10.21227/ny9p-rv41},
author = {Timothy Roberts },
publisher = {IEEE Dataport},
title = {A Time-Scale Modification Dataset with Subjective Quality Labels},
year = {2020} }
TY - DATA
T1 - A Time-Scale Modification Dataset with Subjective Quality Labels
AU - Timothy Roberts
PY - 2020
PB - IEEE Dataport
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Timothy Roberts. (2020). A Time-Scale Modification Dataset with Subjective Quality Labels. IEEE Dataport. http://dx.doi.org/10.21227/ny9p-rv41
Timothy Roberts, 2020. A Time-Scale Modification Dataset with Subjective Quality Labels. Available at: http://dx.doi.org/10.21227/ny9p-rv41.
Timothy Roberts. (2020). "A Time-Scale Modification Dataset with Subjective Quality Labels." Web.
1. Timothy Roberts. A Time-Scale Modification Dataset with Subjective Quality Labels [Internet]. IEEE Dataport; 2020. Available from : http://dx.doi.org/10.21227/ny9p-rv41
Timothy Roberts. "A Time-Scale Modification Dataset with Subjective Quality Labels." doi: 10.21227/ny9p-rv41