Abstract

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. The source material contains speech, solo harmonic and percussive instruments, sound effects, and a range of music genres. Ratings (42 529) were collected from 633 sessions using laboratory and remote collection methods. Analysis of results shows no correlation between age and quality of rating; expert and non-expert listeners to be equivalent; minor differences between participants with and without hearing issues; and minimal differences between testing modalities. A comparison of published objective measures and subjective scores shows the objective measures to be poor indicators of subjective quality. Initial results for a retrained objective measure of quality are presented with results approaching average root mean squared error loss and Pearson correlation values of subjective sessions. The labeled dataset is available at http://ieee-dataport.org/1987.

Instructions:

When using this dataset, please use the following citation:

@article{doi:10.1121/10.0001567,
author = {Roberts,Timothy  and Paliwal,Kuldip K. },
title = {A time-scale modification dataset with subjective quality labels},
journal = {The Journal of the Acoustical Society of America},
volume = {148},
number = {1},
pages = {201-210},
year = {2020},
doi = {10.1121/10.0001567},
URL = {https://doi.org/10.1121/10.0001567},
eprint = {https://doi.org/10.1121/10.0001567}
}

Audio files are named using the following structure: SourceName_TSMmethod_TSMratio_per.wav and split into multiple zip files.

For 'TSMmethod', PV is the Phase Vocoder algorithm, PV_IPL is the Identity Phase Locking Phase Vocoder algorithm, WSOLA is the Waveform Similarity Overlap-Add algorithm, FESOLA is the Fuzzy Epoch Synchronous Overlap-Add algorithm, HPTSM is the Harmonic-Percussive Separation Time-Scale Modification algorithm and uTVS is the Mel-Scale Sub-Band Modelling Filterbank algorithm. Elastique is the z-Plane Elastique algorithm, NMF is the Non-Negative Matrix Factorization algorithm and FuzzyPV is the Phase Vocoder algorithm using Fuzzy Classification of Spectral Bins.

TSM ratios range from 33% to 192% for training files, 20% to 200% for testing files and 22% to 220% for evaluation files.

Train: Contains 5280 processed files for training neural networks

Test: Contains 240 processed files for testing neural networks

Ref_Train: Contains the 88 reference files for the processed training files

Ref_Test: Contains the 20 reference files for the processed testing files

Eval: Contains 6000 processed files for evaluating TSM methods.  The 20 reference test files were processed at 20 time-scales using the following methods:

```
Phase Vocoder (PV)
```

Identity Phase-Locking Phase Vocoder (IPL)

Scaled Phase-Locking Phase Vocoder (SPL)

```
Phavorit IPL and SPL
```

Phase Vocoder with Fuzzy Classification of Spectral Bins (FuzzyPV)

```
Waveform Similarity Overlap-Add (WSOLA)
```
```
Epoch Synchronous Overlap-Add (ESOLA)
```

Fuzzy Epoch Synchronous Overlap-Add (FESOLA)

Driedger's Identity Phase-Locking Phase Vocoder (DrIPL)

Harmonic Percussive Separation Time-Scale Modification (HPTSM)

uTVS used in Subjective testing (uTVS_Subj)

```
updated uTVS (uTVS)
```

Non-Negative Matrix Factorization Time-Scale Modification (NMFTSM)

```
Elastique.
```

TSM_MOS_Scores.mat is a version 7 MATLAB save file and contains a struct called data that has the following fields:

test_loc: Legacy folder location of the test file.

```
test_name: Name of the test file.
```

ref_loc: Legacy folder location of reference file.

```
ref_name: Name of the reference file.
```

method: The method used for processing the file.

TSM: The time-scale ratio (in percent) used for processing the file. 100(%) is unity processing. 50(%) is half speed, 200(%) is double speed.

```
MeanOS: Normalized Mean Opinion Score.
```

MedianOS: Normalized Median Opinion Score.

```
std: Standard Deviation of MeanOS.
```

MeanOS_RAW: Mean Opinion Score before normalization.

MedianOS_RAW: Median Opinion Scores before normalization.

std_RAW: Standard Deviation of MeanOS before normalization.

TSM_MOS_Scores.csv is a csv containing the same fields as columns.

Source Code and method implementations are available at www.github.com/zygurt/TSM

Please Note: Labels for the files will be uploaded after paper publication.

Dataset Files

Reference Training Files ref_train.zip (42.67 MB)
Reference Testing Files ref_test.zip (5.13 MB)
Processed Training Files train.zip (1.91 GB)
Processed Testing Files test.zip (96.02 MB)
Unlabelled Evaluation Files eval.zip (2.23 GB)
Final MOS values TSM_MOS_Scores.mat (556.71 kB)
Final MOS values TSM_MOS_Scores.csv (669.53 kB)

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A Time-Scale Modification Dataset with Subjective Quality Labels

Abstract

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