{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T23:42:00Z","timestamp":1769557320640,"version":"3.49.0"},"reference-count":55,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,4,3]],"date-time":"2020-04-03T00:00:00Z","timestamp":1585872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The project\/research was financed in the framework of the project Lublin University of Technology; Regional Excellence Initiative, funded by the Polish Ministry of Science and Higher Education (contract no.030\/RID\/2018\/19).","award":["contract no.030\/RID\/2018\/19"],"award-info":[{"award-number":["contract no.030\/RID\/2018\/19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper relates to the separation of single channel source signals from a single mixed signal by means of independent component analysis (ICA). The proposed idea lies in a time-frequency representation of the mixed signal and the use of ICA on spectral rows corresponding to different time intervals. In our approach, in order to reconstruct true sources, we proposed a novelty idea of grouping statistically independent time-frequency domain (TFD) components of the mixed signal obtained by ICA. The TFD components are grouped by hierarchical clustering and k-mean partitional clustering. The distance between TFD components is measured with the classical Euclidean distance and the    \u03b2    distance of Gaussian distribution introduced by as. In addition, the TFD components are grouped by minimizing the negentropy of reconstructed constituent signals. The proposed method was used to separate source signals from single audio mixes of two- and three-component signals. The separation was performed using algorithms written by the authors in Matlab. The quality of obtained separation results was evaluated by perceptual tests. The tests showed that the automated separation requires qualitative information about time-frequency characteristics of constituent signals. The best separation results were obtained with the use of the    \u03b2    distance of Gaussian distribution, a distance measure based on the knowledge of the statistical nature of spectra of original constituent signals of the mixed signal.<\/jats:p>","DOI":"10.3390\/s20072019","type":"journal-article","created":{"date-parts":[[2020,4,7]],"date-time":"2020-04-07T03:58:39Z","timestamp":1586231919000},"page":"2019","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Single Channel Source Separation with ICA-Based Time-Frequency Decomposition"],"prefix":"10.3390","volume":"20","author":[{"given":"Dariusz","family":"Mika","sequence":"first","affiliation":[{"name":"Institute of Technical Sciences and Aviation, The State School of Higher Education in Chelm, 22-100 Chelm, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3598-2860","authenticated-orcid":false,"given":"Grzegorz","family":"Budzik","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, Rzeszow University of Technology, 35-959 Rzeszow, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8845-0764","authenticated-orcid":false,"given":"Jerzy","family":"J\u00f3zwik","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, Lublin University of Technology, 20-618 Lublin, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,3]]},"reference":[{"key":"ref_1","unstructured":"Al-Baddai, S., Al-Subari, K., Tom\u00e9, A.M., Volberg, G., and Lang, E.W. 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