A robust high-resolution time–frequency representation based on the local optimization of the short-time fractional Fourier transform

Gerkmann, 2015, Phase processing for single-channel speech enhancement: history and recent advances, IEEE Signal Process. Mag., 32, 55, 10.1109/MSP.2014.2369251 Ouelha, 2017, An efficient inverse short-time Fourier transform algorithm for improved signal reconstruction by time–frequency synthesis: optimality and computational issues, Digit. Signal Process., 65, 81, 10.1016/j.dsp.2017.03.002 Djurović, 2014, Robust time–frequency representation based on the signal normalization and concentration measures, Signal Process., 104, 424, 10.1016/j.sigpro.2014.05.005 Boashash, 2015 Hon, 2012, Enhancing the resolution of the spectrogram based on a simple adaptation procedure, IEEE Trans. Signal Process., 60, 5566, 10.1109/TSP.2012.2208637 Assous, 2012, Evaluation of the modified S-transform for time–frequency synchrony analysis and source localisation, EURASIP J. Adv. Signal Process., 2012, 1, 10.1186/1687-6180-2012-49 Sejdic, 2008, A window width optimized S-transform, EURASIP J. Adv. Signal Process., 2008, 59 Djurović, 2008, Frequency-based window width optimization for S-transform, AEÜ, Int. J. Electron. Commun., 62, 245, 10.1016/j.aeue.2007.03.014 Stankovic, 2013 Almeida, 1994, The fractional Fourier transform and time–frequency representations, IEEE Trans. Signal Process., 42, 3084, 10.1109/78.330368 Catherall, 2010, High resolution spectrograms using a component optimized short-term fractional Fourier transform, Signal Process., 90, 1591, 10.1016/j.sigpro.2009.11.004 Bultheel, 2002 Stanković, 2003, Time–frequency signal analysis based on the windowed fractional Fourier transform, Signal Process., 83, 2459, 10.1016/S0165-1684(03)00197-X Ran, 2010, Short-time fractional Fourier transform and its applications, IEEE Trans. Signal Process., 58, 2568, 10.1109/TSP.2009.2028095 Starosielec, 2014, Discrete-time windows with minimal RMS bandwidth for given RMS temporal width, Signal Process., 102, 240, 10.1016/j.sigpro.2014.03.033 Chen, 2012, A novel optimal STFrFT and its application in seismic signal processing, 328 Serbes, 2010, Optimum signal and image recovery by the method of alternating projections in fractional Fourier domains, Commun. Nonlinear Sci. Numer. Simul., 15, 675, 10.1016/j.cnsns.2009.05.013 Akan, 2007, A discrete fractional Gabor expansion for multi-component signals, AEÜ, Int. J. Electron. Commun., 61, 279, 10.1016/j.aeue.2006.05.001 Baraniuk, 2001, Measuring time–frequency information content using the Rényi entropies, IEEE Trans. Inf. Theory, 47, 1391, 10.1109/18.923723 Boashash, 2003, Resolution measure criteria for the objective assessment of the performance of quadratic time–frequency distributions, IEEE Trans. Signal Process., 51, 1253, 10.1109/TSP.2003.810300 Stanković, 2015, Measuring time–frequency distributions concentration, 401 Shafi, 2010, Validity-guided fuzzy clustering evaluation for neural network-based time–frequency reassignment, EURASIP J. Adv. Signal Process., 2010, 10.1155/2010/636858 Malnar, 2015, Automatic quality assessment and optimisation of quadratic time–frequency representations, Electron. Lett., 51, 1029, 10.1049/el.2015.0489 Awal, 2017, An automatic fast optimization of quadratic time–frequency distribution using the hybrid genetic algorithm, Signal Process., 131, 134, 10.1016/j.sigpro.2016.08.017 Boashash, 2016, Automatic signal abnormality detection using time–frequency features and machine learning: a newborn EEG seizure case study, Knowl.-Based Syst., 106, 38, 10.1016/j.knosys.2016.05.027 Sucic, 2011, Estimating the number of components of a multicomponent nonstationary signal using the short-term time–frequency Rényi entropy, EURASIP J. Adv. Signal Process., 2011, 1, 10.1186/1687-6180-2011-125 Hurley, 2009, Comparing measures of sparsity, IEEE Trans. Inf. Theory, 55, 4723, 10.1109/TIT.2009.2027527 Flandrin, 2010, Time–frequency energy distributions meet compressed sensing, IEEE Trans. Signal Process., 58, 2974, 10.1109/TSP.2010.2044839 Stanković, 2014, Instantaneous frequency in time–frequency analysis: enhanced concepts and performance of estimation algorithms, Digit. Signal Process., 35, 1, 10.1016/j.dsp.2014.09.008 Barkat, 2004, Algorithms for blind components separation and extraction from the time–frequency distribution of their mixture, EURASIP J. Appl. Signal Process., 2004 Awal, 2016, EEG background features that predict outcome in term neonates with Hypoxic Ischaemic Encephalopathy: a structured review, Clin. Neurophysiol., 127, 285, 10.1016/j.clinph.2015.05.018 Awal, 2014, Detection of neonatal EEG burst-suppression using a time–frequency approach, 1 Boashash, 2013, Time–frequency processing of nonstationary signals: advanced TFD design to aid diagnosis with highlights from medical applications, IEEE Signal Process. Mag., 30, 108, 10.1109/MSP.2013.2265914 Sejdić, 2009, Time–frequency feature representation using energy concentration: an overview of recent advances, Digit. Signal Process., 19, 153, 10.1016/j.dsp.2007.12.004 Stankovic, 2014, From the STFT to the Wigner distribution [Lecture Notes], IEEE Signal Process. Mag., 31, 163, 10.1109/MSP.2014.2301791 Gholami, 2013, Sparse time–frequency decomposition and some applications, IEEE Trans. Geosci. Remote Sens., 51, 3598, 10.1109/TGRS.2012.2220144 Lerga, 2017, Algorithm based on the short-term Renyi entropy and IF estimation for noisy EEG signals analysis, Comput. Biol. Med., 80, 1, 10.1016/j.compbiomed.2016.11.002 Boashash, 2017, An Improved design of high-resolution quadratic time–frequency distributions for the analysis of nonstationary multicomponent signals using directional compact kernels, IEEE Trans. Signal Process., 65, 2701, 10.1109/TSP.2017.2669899 Mason, 2002, Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: statistical significance and interpretation, Q. J. R. Meteorol. Soc., 128, 2145, 10.1256/003590002320603584