{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T17:07:13Z","timestamp":1785604033307,"version":"3.56.0"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T00:00:00Z","timestamp":1676073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Higher Education Malaysia for Fundamental Research","award":["FRGS\/1\/2020\/ICT03\/USM\/02\/1"],"award-info":[{"award-number":["FRGS\/1\/2020\/ICT03\/USM\/02\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Blind source separation (BSS) recovers source signals from observations without knowing the mixing process or source signals. Underdetermined blind source separation (UBSS) occurs when there are fewer mixes than source signals. Sparse component analysis (SCA) is a general UBSS solution that benefits from sparse source signals which consists of (1) mixing matrix estimation and (2) source recovery estimation. The first stage of SCA is crucial, as it will have an impact on the recovery of the source. Single-source points (SSPs) were detected and clustered during the process of mixing matrix estimation. Adaptive time\u2013frequency thresholding (ATFT) was introduced to increase the accuracy of the mixing matrix estimations. ATFT only used significant TF coefficients to detect the SSPs. After identifying the SSPs, hierarchical clustering approximates the mixing matrix. The second stage of SCA estimated the source recovery using least squares methods. The mixing matrix and source recovery estimations were evaluated using the error rate and mean squared error (MSE) metrics. The experimental results on four bioacoustics signals using ATFT demonstrated that the proposed technique outperformed the baseline method, Zhen\u2019s method, and three state-of-the-art methods over a wide range of signal-to-noise ratio (SNR) ranges while consuming less time.<\/jats:p>","DOI":"10.3390\/s23042060","type":"journal-article","created":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T02:14:11Z","timestamp":1676254451000},"page":"2060","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Sparse Component Analysis (SCA) Based on Adaptive Time\u2014Frequency Thresholding for Underdetermined Blind Source Separation (UBSS)"],"prefix":"10.3390","volume":"23","author":[{"given":"Norsalina","family":"Hassan","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Politeknik Seberang Perai, Jalan Permatang Pauh, Bukit Mertajam 13700, Pulau Pinang, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4392-2895","authenticated-orcid":false,"given":"Dzati Athiar","family":"Ramli","sequence":"additional","affiliation":[{"name":"School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/0165-1684(94)90029-9","article-title":"Independent component analysis, A new concept?","volume":"36","author":"Comon","year":"1994","journal-title":"Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107590","DOI":"10.1016\/j.sigpro.2020.107590","article-title":"Time and frequency based sparse bounded component analysis algorithms for convolutive mixtures","volume":"173","author":"Babatas","year":"2020","journal-title":"Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e12","DOI":"10.1017\/ATSIP.2019.5","article-title":"A review of blind source separation methods: Two converging routes to ILRMA originating from ICA and NMF","volume":"8","author":"Sawada","year":"2019","journal-title":"APSIPA Trans. Signal Inf. Process."},{"key":"ref_4","unstructured":"Wang, L. (2019). A Study on Multi-Subspace Representation of Nonlinear Mixture with Application in Blind Source Separation: Modeling and Performance Analysis. [Ph.D. Thesis, Keio University]."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"125011","DOI":"10.1088\/1361-6501\/ab3054","article-title":"Sparse component analysis with optimized clustering for underdetermined blind modal identification","volume":"30","author":"Guan","year":"2019","journal-title":"Meas. Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Guo, Q., Ruan, G., and Liao, Y. (2017). A time-frequency domain underdetermined blind source separation algorithm for MIMO radar signals. Symmetry, 9.","DOI":"10.3390\/sym9070104"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Guo, Q., Li, C., and Ruan, G. (2018). Mixing matrix estimation of underdetermined blind source separation based on data field and improved FCM clustering. Symmetry, 10.","DOI":"10.3390\/sym10010021"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lu, J., Cheng, W., and Zi, Y. (2019). A novel underdetermined blind source separation method and its application to source contribution quantitative estimation. Sensors, 19.","DOI":"10.3390\/s19061413"},{"key":"ref_9","first-page":"1","article-title":"An underdetermined blind source separation method with application to modal identification","volume":"2019","author":"Yu","year":"2019","journal-title":"Shock Vib."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Qiu, P., Zhang, Y., Wang, Y., Yin, Q., and Wang, Q. (2021, January 26\u201328). Underdetermined Speech Source Separation Based on Hybrid Clustering. Proceedings of the 2021 40th Chinese Control Conference (CCC), Shanghai, China.","DOI":"10.23919\/CCC52363.2021.9549721"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.aeue.2017.04.025","article-title":"Underdetermined blind source separation by a novel time\u2013frequency method","volume":"77","author":"Su","year":"2017","journal-title":"AEU -Int. J. Electron. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1007\/s11760-019-01632-z","article-title":"A novel mixing matrix estimation algorithm in instantaneous underdetermined blind source separation","volume":"14","author":"Li","year":"2020","journal-title":"Signal Image Video Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"13061","DOI":"10.1007\/s11042-020-08635-w","article-title":"Underdetermined mixing matrix estimation based on artificial bee colony optimization and single-source-point detection","volume":"79","author":"He","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/TSP.2005.861743","article-title":"Underdetermined blind source separation based on sparse representation","volume":"54","author":"Li","year":"2006","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_15","first-page":"1799","article-title":"A new underdetermined blind source separation algorithm under the anechoic mixing model","volume":"2019","author":"Liu","year":"2016","journal-title":"Int. Conf. Signal Process. Proc. ICSP"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3102","DOI":"10.1109\/TNNLS.2016.2610960","article-title":"Underdetermined Blind Source Separation Using Sparse Coding","volume":"28","author":"Zhen","year":"2017","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1007\/s00034-018-0910-9","article-title":"A Novel Underdetermined Source Recovery Algorithm Based on k-Sparse Component Analysis","volume":"38","author":"Eqlimi","year":"2019","journal-title":"Circuits Syst. Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"095001","DOI":"10.1088\/1361-6501\/ab816f","article-title":"Underdetermined convolutive blind source separation in time-frequency domain based on single source points and experimental validation","volume":"31","author":"Cheng","year":"2020","journal-title":"Meas. Sci. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1109\/TASLP.2022.3145304","article-title":"Convolutive Transfer Function-Based Multichannel Nonnegative Matrix Factorization for Overdetermined Blind Source Separation","volume":"30","author":"Wang","year":"2022","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_20","first-page":"89","article-title":"Sparse component analysis for blind source separation with less sensors than sources","volume":"2003","author":"Li","year":"2003","journal-title":"Ica"},{"key":"ref_21","first-page":"46","article-title":"Underdetermined blind source separation of non-disjoint nonstationary sources in the time-frequency domain","volume":"Volume 1","author":"Belouchrani","year":"2005","journal-title":"Proceedings of the 8th International Symposium on Signal Processing and its Applications, ISSPA"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2881","DOI":"10.1007\/s00034-015-0173-7","article-title":"Underdetermined BSS Based on K-means and AP Clustering","volume":"35","author":"He","year":"2016","journal-title":"Circuits Syst. Signal Process."},{"key":"ref_23","first-page":"337","article-title":"Mixing matrix estimation algorithm for underdetermined instantaneous mixing model","volume":"15","author":"Shi","year":"2019","journal-title":"Int. J. Perform. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1762","DOI":"10.1016\/j.sigpro.2009.03.017","article-title":"An algorithm for mixing matrix estimation in instantaneous blind source separation","volume":"89","author":"Reju","year":"2009","journal-title":"Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1016\/j.sigpro.2005.02.010","article-title":"A time-frequency blind signal separation method applicable to underdetermined mixtures of dependent sources","volume":"85","author":"Abrard","year":"2005","journal-title":"Signal Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2604","DOI":"10.1109\/TSP.2009.2017570","article-title":"Underdetermined blind source separation based on subspace representation","volume":"57","author":"Kim","year":"2009","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2179","DOI":"10.5370\/JEET.2015.10.5.2179","article-title":"Underdetermined blind source separation from time-delayed mixtures based on prior information exploitation","volume":"10","author":"Zhang","year":"2015","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1007\/s00034-018-0930-5","article-title":"A Mixing Matrix Estimation Algorithm for the Time-Delayed Mixing Model of the Underdetermined Blind Source Separation Problem","volume":"38","author":"Ye","year":"2019","journal-title":"Circuits Syst. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9011","DOI":"10.1007\/s00500-019-04430-4","article-title":"Underdetermined blind source separation using CapsNet","volume":"24","author":"Kumar","year":"2020","journal-title":"Soft Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"115256","DOI":"10.1109\/ACCESS.2021.3105538","article-title":"An Effective Two-Stage Clustering Method for Mixing Matrix Estimation in Instantaneous Underdetermined Blind Source Separation and Its Application in Fault Diagnosis","volume":"9","author":"Wang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.knosys.2019.02.035","article-title":"Structure regularized sparse coding for data representation","volume":"174","author":"Wang","year":"2019","journal-title":"Knowl. -Based Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-02790-2","article-title":"BioCPPNet: Automatic bioacoustic source separation with deep neural networks","volume":"11","author":"Bermant","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_33","unstructured":"(2022, December 10). Frogs of Australia > Taxonomy. Available online: https:\/\/frogs.org.au\/frogs\/search.php."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1830","DOI":"10.1109\/TSP.2004.828896","article-title":"Blind separation of speech mixtures via time-frequency masking","volume":"52","author":"Yilmaz","year":"2004","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/TASL.2009.2024380","article-title":"Underdetermined convolutive blind source separation via time-frequency masking","volume":"18","author":"Reju","year":"2010","journal-title":"IEEE Trans. Audio Speech Lang. Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/2060\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:31:58Z","timestamp":1760121118000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/2060"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,11]]},"references-count":35,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23042060"],"URL":"https:\/\/doi.org\/10.3390\/s23042060","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,11]]}}}