The distributed generation, along with the deregulation of the Smart Grid, have created a great concern on Power Quality (PQ), as it has a direct impact on utilities and customers, as well as effects on the sinusoidal signal of the power line. The a priori unknown features of the distributed energy resources (DER) introduce non-linear behaviours in loads associated to a variety of PQ disturbances.


This dataset contains the ontologies and instruments developed in the MIIDAS Project


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.


When using this dataset, please use the following citation:

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 = {},
eprint = {}


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

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


Design of novel RF front-end hardware architectures and their associated measurement algorithms.Research objectives, includes:RO1: Novel architecture based upon Adaptive Wavelet Band-pass Sampling (AWBS) of RF Analog-to-Information Conversion (AIC).RO2: Integration of AWBS for increasing the wideband sensing capabilities of real-time spectrum analyzers by using AICs.RO3: Propose online calibration methods and algorithms for front-end hardware non-idealities compensation.RO4: Design a hardware prototype aimed to perform real-time spectrum sensing with 1 GHz real-time bandwidth.


The aim of the database consists of providing the researchers with a collection of power quality real-life impulsive events to test experiments and measurement instruments. The dataset provides signals recordings from the power network of the University of Cádiz during the last five years (electrical network according to the UNE-EN-50160: 2011).

The dataset offers a diversity of real impulsive events, which are specifically acquired in order to test Power Quality Instruments according to the UNE-IEC 61000-4-11: 2005.