  Objective evaluation of audio processed with Time-Scale Modification (TSM)
remains an open problem. Recently, a dataset of time-scaled audio with
subjective quality labels was published and used to create an initial objective
measure of quality. In this paper, an improved objective measure of quality for
time-scaled audio is proposed. The measure uses hand-crafted features and a
fully connected network to predict subjective mean opinion scores. Basic and
Advanced Perceptual Evaluation of Audio Quality features are used in addition
to nine features specific to TSM artefacts. Six methods of alignment are
explored, with interpolation of the reference magnitude spectrum to the length
of the test magnitude spectrum giving the best performance. The proposed
measure achieves a mean Root Mean Squared Error of 0.487 and a mean Pearson
correlation of 0.865, equivalent to 98th and 82nd percentiles of subjective
sessions respectively. The proposed measure is used to evaluate time-scale
modification algorithms, finding that Elastique gives the highest objective
quality for Solo instrument and voice signals, while the Identity Phase-Locking
Phase Vocoder gives the highest objective quality for music signals and the
best overall quality. The objective measure is available at
https://www.github.com/zygurt/TSM.
