{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T08:09:03Z","timestamp":1746864543270,"version":"3.37.3"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T00:00:00Z","timestamp":1663804800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T00:00:00Z","timestamp":1663804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100011880","name":"New Zealand Institute for Plant and Food Research Limited","doi-asserted-by":"publisher","award":["JPJSBP120201002"],"award-info":[{"award-number":["JPJSBP120201002"]}],"id":[{"id":"10.13039\/100011880","id-type":"DOI","asserted-by":"publisher"}]},{"name":"JSPS KAKENHI","award":["19K20306","19H01116","21H05054"],"award-info":[{"award-number":["19K20306","19H01116","21H05054"]}]},{"DOI":"10.13039\/501100020963","name":"Moonshot Research and Development Program","doi-asserted-by":"publisher","award":["JPMJPS2011"],"award-info":[{"award-number":["JPMJPS2011"]}],"id":[{"id":"10.13039\/501100020963","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EURASIP J. Adv. Signal Process."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Rank-constrained spatial covariance matrix estimation (RCSCME) is a blind speech extraction method utilized under the condition that one-directional target speech and diffuse background noise are mixed. In this paper, we propose a new model extension of RCSCME. RCSCME simultaneously conducts both the deficient rank-1 component complementation of the diffuse noise spatial covariance matrix, which is incompletely estimated by preprocessing methods such as independent low-rank matrix analysis, and the estimation of the source model parameters. In the conventional RCSCME, between the two parameters constituting the deficient rank-1 component, only the scale is estimated, whereas the other parameter, the deficient basis, is fixed in advance; however, how to choose the fixed deficient basis is not unique. In the proposed RCSCME model, we also regard the deficient basis as a parameter to estimate. As the generative model of an observed signal, we utilized the super-Gaussian generalized Gaussian distribution, which achieves better separation performance than the Gaussian distribution in the conventional RCSCME. Assuming the model, we derive new majorization-minimization (MM)- and majorization-equalization (ME)-algorithm-based update rules for the deficient basis. In particular, among innumerable ME-algorithm-based update rules, we successfully find an ME-algorithm-based update rule with a mathematical proof supporting the fact that the step of the update rule is larger than that of the MM-algorithm-based update rule. We confirm that the proposed method outperforms conventional methods under several simulated noise conditions and a real noise condition.<\/jats:p>","DOI":"10.1186\/s13634-022-00905-z","type":"journal-article","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T23:03:41Z","timestamp":1663887821000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deficient-basis-complementary rank-constrained spatial covariance matrix estimation based on multivariate generalized Gaussian distribution for blind speech extraction"],"prefix":"10.1186","volume":"2022","author":[{"given":"Yuto","family":"Kondo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuki","family":"Kubo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8102-3110","authenticated-orcid":false,"given":"Norihiro","family":"Takamune","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daichi","family":"Kitamura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroshi","family":"Saruwatari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,22]]},"reference":[{"issue":"e12","key":"905_CR1","first-page":"1","volume":"8","author":"H Sawada","year":"2019","unstructured":"H. Sawada, N. Ono, H. Kameoka, D. Kitamura, H. Saruwatari, A review of blind source separation methods: two converging routes to ILRMA originating from ICA and NMF. APSIPA Trans. Signal Inf. Process. 8(e12), 1\u201314 (2019)","journal-title":"APSIPA Trans. Signal Inf. Process."},{"issue":"4","key":"905_CR2","first-page":"650","volume":"17","author":"Y Takahashi","year":"2009","unstructured":"Y. Takahashi, T. Takatani, K. Osako, H. Saruwatari, K. Shikano, Blind spatial subtraction array for speech enhancement in noisy environment. IEEE Trans. ASLP 17(4), 650\u2013664 (2009)","journal-title":"IEEE Trans. ASLP"},{"issue":"4","key":"905_CR3","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1109\/TSP.2018.2887185","volume":"67","author":"Z Koldovsk\u00fd","year":"2019","unstructured":"Z. Koldovsk\u00fd, P. Tichavsk\u00fd, Gradient algorithms for complex non-Gaussian independent component\/vector extraction, question of convergence. IEEE Trans. SP 67(4), 1050\u20131064 (2019)","journal-title":"IEEE Trans. SP"},{"issue":"1\u20133","key":"905_CR4","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/S0925-2312(98)00047-2","volume":"22","author":"P Smaragdis","year":"1998","unstructured":"P. Smaragdis, Blind separation of convolved mixtures in the frequency domain. Neurocomputing 22(1\u20133), 21\u201334 (1998)","journal-title":"Neurocomputing"},{"issue":"2","key":"905_CR5","first-page":"109","volume":"11","author":"S Araki","year":"2003","unstructured":"S. Araki, R. Mukai, S. Makino, T. Nishikawa, H. Saruwatari, The fundamental limitation of frequency domain blind source separation for convolutive mixtures of speech. IEEE Trans. ASP 11(2), 109\u2013116 (2003)","journal-title":"IEEE Trans. ASP"},{"issue":"2","key":"905_CR6","first-page":"666","volume":"14","author":"H Saruwatari","year":"2006","unstructured":"H. Saruwatari, T. Kawamura, T. Nishikawa, A. Lee, K. Shikano, Blind source separation based on a fast-convergence algorithm combining ICA and beamforming. IEEE Trans. ASLP 14(2), 666\u2013678 (2006)","journal-title":"IEEE Trans. ASLP"},{"key":"905_CR7","doi-asserted-by":"crossref","unstructured":"A. Hiroe, Solution of permutation problem in frequency domain ICA using multivariate probability density functions, in Proceedings of ICA (2006), pp. 601\u2013608","DOI":"10.1007\/11679363_75"},{"issue":"1","key":"905_CR8","first-page":"70","volume":"15","author":"T Kim","year":"2007","unstructured":"T. Kim, H.T. Attias, S.-Y. Lee, T.-W. Lee, Blind source separation exploiting higher-order frequency dependencies. IEEE Trans. ASLP 15(1), 70\u201379 (2007)","journal-title":"IEEE Trans. ASLP"},{"issue":"9","key":"905_CR9","first-page":"1626","volume":"24","author":"D Kitamura","year":"2016","unstructured":"D. Kitamura, N. Ono, H. Sawada, H. Kameoka, H. Saruwatari, Determined blind source separation unifying independent vector analysis and nonnegative matrix factorization. IEEE\/ACM Trans. ASLP 24(9), 1626\u20131641 (2016)","journal-title":"IEEE\/ACM Trans. ASLP"},{"issue":"1","key":"905_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13634-018-0549-5","volume":"2018","author":"D Kitamura","year":"2018","unstructured":"D. Kitamura, S. Mogami, Y. Mitsui, N. Takamune, H. Saruwatari, N. Ono, Y. Takahashi, K. Kondo, Generalized independent low-rank matrix analysis using heavy-tailed distributions for blind source separation. EURASIP J. Adv. Signal Process. 2018(1), 1\u201328 (2018)","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"905_CR11","doi-asserted-by":"crossref","unstructured":"R. Ikeshita, Y. Kawaguchi, Independent low-rank matrix analysis based on multivariate complex exponential power distribution, in Proceedings of ICASSP (2018), pp. 741\u2013745","DOI":"10.1109\/ICASSP.2018.8461387"},{"issue":"7","key":"905_CR12","first-page":"1830","volume":"18","author":"NQK Duong","year":"2010","unstructured":"N.Q.K. Duong, E. Vincent, R. Gribonval, Under-determined reverberant audio source separation using a full-rank spatial covariance model. IEEE Trans. ASLP 18(7), 1830\u20131840 (2010)","journal-title":"IEEE Trans. ASLP"},{"issue":"3","key":"905_CR13","first-page":"550","volume":"18","author":"A Ozerov","year":"2010","unstructured":"A. Ozerov, C. F\u00e9votte, Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation. IEEE Trans. ASLP 18(3), 550\u2013563 (2010)","journal-title":"IEEE Trans. ASLP"},{"issue":"5","key":"905_CR14","first-page":"971","volume":"21","author":"H Sawada","year":"2013","unstructured":"H. Sawada, H. Kameoka, S. Araki, N. Ueda, Multichannel extensions of non-negative matrix factorization with complex-valued data. IEEE Trans. ASLP 21(5), 971\u2013982 (2013)","journal-title":"IEEE Trans. ASLP"},{"key":"905_CR15","doi-asserted-by":"crossref","unstructured":"K. Kitamura, Y. Bando, K. Itoyama, K. Yoshii, Student\u2019s t multichannel nonnegative matrix factorization for blind source separation, in Proceedings of IWAENC (2016)","DOI":"10.1109\/IWAENC.2016.7602889"},{"key":"905_CR16","doi-asserted-by":"crossref","unstructured":"N. Ito, T. Nakatani, FastMNMF: joint diagonalization based accelerated algorithms for multichannel nonnegative matrix factorization, in Proceedings of ICASSP (2019), pp. 371\u2013375","DOI":"10.1109\/ICASSP.2019.8682291"},{"key":"905_CR17","first-page":"2610","volume":"28","author":"K Sekiguchi","year":"2020","unstructured":"K. Sekiguchi, Y. Bando, A.A. Nugraha, K. Yoshii, T. Kawahara, Fast multichannel nonnegative matrix factorization with directivity-aware jointly-diagonalizable spatial covariance matrices for blind source separation. IEEE Trans. ASLP 28, 2610\u20132625 (2020)","journal-title":"IEEE Trans. ASLP"},{"key":"905_CR18","doi-asserted-by":"crossref","unstructured":"K. Kamo, Y. Kubo, N. Takamune, D. Kitamura, H. Saruwatari, Y. Takahashi, K. Kondo, Joint-diagonalizability-constrained multichannel nonnegative matrix factorization based on multivariate complex Student\u2019s t-distribution, in Proceedings of APSIPA (2020)","DOI":"10.1016\/j.sigpro.2021.108183"},{"key":"905_CR19","first-page":"1948","volume":"28","author":"Y Kubo","year":"2020","unstructured":"Y. Kubo, N. Takamune, D. Kitamura, H. Saruwatari, Blind speech extraction based on rank-constrained spatial covariance matrix estimation with multivariate generalized Gaussian distribution. IEEE\/ACM Trans. ASLP 28, 1948\u20131963 (2020)","journal-title":"IEEE\/ACM Trans. ASLP"},{"issue":"1","key":"905_CR20","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1080\/10618600.2000.10474866","volume":"9","author":"DR Hunter","year":"2000","unstructured":"D.R. Hunter, K. Lange, Quantile regression via an MM algorithm. J. Comput. Graph. Stat. 9(1), 60\u201377 (2000)","journal-title":"J. Comput. Graph. Stat."},{"issue":"9","key":"905_CR21","doi-asserted-by":"publisher","first-page":"2421","DOI":"10.1162\/NECO_a_00168","volume":"23","author":"C F\u00e9votte","year":"2011","unstructured":"C. F\u00e9votte, J. Idier, Algorithms for nonnegative matrix factorization with the \u03b2-divergence. Neural Comput. 23(9), 2421\u20132456 (2011)","journal-title":"Neural Comput."},{"key":"905_CR22","doi-asserted-by":"crossref","unstructured":"Y. Kondo, Y. Kubo, N. Takamune, D. Kitamura, H. Saruwatari, Deficient basis estimation of noise spatial covariance matrix for rank-constrained spatial covariance matrix estimation method in blind speech extraction, in Proceedings of ICASSP (2021), pp. 806\u2013810","DOI":"10.1109\/ICASSP39728.2021.9414479"},{"issue":"3","key":"905_CR23","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1080\/03610929808832115","volume":"27","author":"E G\u00f3mez","year":"1998","unstructured":"E. G\u00f3mez, M. Gomez-Viilegas, J.M. Mar\u00edn, A multivariate generalization of the power exponential family of distributions. Commun. Stat. Theory Methods 27(3), 589\u2013600 (1998)","journal-title":"Commun. Stat. Theory Methods"},{"issue":"1\u20134","key":"905_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0925-2312(00)00345-3","volume":"41","author":"N Murata","year":"2001","unstructured":"N. Murata, S. Ikeda, A. Ziehe, An approach to blind source separation based on temporal structure of speech signals. Neurocomputing 41(1\u20134), 1\u201324 (2001)","journal-title":"Neurocomputing"},{"key":"905_CR25","first-page":"341","volume":"10","author":"B Kulis","year":"2009","unstructured":"B. Kulis, M.A. Sustik, I.S. Dhillon, Low-rank kernel learning with Bregman matrix divergences. J. Mach. Learn. Res. 10, 341\u2013376 (2009)","journal-title":"J. Mach. Learn. Res."},{"issue":"12","key":"905_CR26","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1016\/j.aml.2006.11.016","volume":"20","author":"J Ding","year":"2007","unstructured":"J. Ding, A. Zhou, Eigenvalues of rank-one updated matrices with some applications. Appl. Math. Lett. 20(12), 1223\u20131226 (2007)","journal-title":"Appl. Math. Lett."},{"issue":"3","key":"905_CR27","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1250\/ast.20.199","volume":"20","author":"K Itou","year":"1999","unstructured":"K. Itou, M. Yamamoto, K. Takeda, T. Takezawa, T. Matsuoka, T. Kobayashi, K. Shikano, S. Itahashi, JNAS: Japanese speech corpus for large vocabulary continuous speech recognition research. J. Acoust. Soc. Jpn. (E) 20(3), 199\u2013206 (1999)","journal-title":"J. Acoust. Soc. Jpn. (E)"},{"key":"905_CR28","unstructured":"J. Thiemann, N. Ito, E. Vincent, DEMAND: a collection of multi-channel recordings of acoustic noise in diverse environments (2013). https:\/\/doi.org\/10.5281\/zenodo.1227121"},{"issue":"11","key":"905_CR29","doi-asserted-by":"publisher","first-page":"2403","DOI":"10.1016\/S0165-1684(01)00128-1","volume":"81","author":"I Cohen","year":"2001","unstructured":"I. Cohen, B. Berdugo, Speech enhancement for non-stationary noise environments. Signal Process. 81(11), 2403\u20132418 (2001)","journal-title":"Signal Process."},{"key":"905_CR30","doi-asserted-by":"crossref","unstructured":"R. Miyazaki, H. Saruwatari, R. Wakisaka, K. Shikano, T. Takatani, Theoretical analysis of parametric blind spatial subtraction array and its application to speech recognition performance prediction, in Proceedings of HSCMA (2011), pp. 19\u201324","DOI":"10.1109\/HSCMA.2011.5942397"},{"key":"905_CR31","doi-asserted-by":"crossref","unstructured":"K. Shimada, Y. Bando, M. Mimura, K. Itoyama, K. Yoshii, T. Kawahara, Unsupervised beamforming based on multichannel nonnegative matrix factorization for noisy speech recognition, in Proceedings of ICASSP (2018), pp. 5734\u20135738","DOI":"10.1109\/ICASSP.2018.8462642"},{"issue":"6","key":"905_CR32","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1109\/TASSP.1984.1164453","volume":"32","author":"Y Ephraim","year":"1984","unstructured":"Y. Ephraim, D. Malah, Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator. IEEE Trans. ASSP 32(6), 1109\u20131121 (1984)","journal-title":"IEEE Trans. ASSP"},{"issue":"4","key":"905_CR33","first-page":"1462","volume":"14","author":"E Vincent","year":"2006","unstructured":"E. Vincent, R. Gribonval, C. F\u00e9votte, Performance measurement in blind audio source separation. IEEE Trans. ASLP 14(4), 1462\u20131469 (2006)","journal-title":"IEEE Trans. ASLP"}],"container-title":["EURASIP Journal on Advances in Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-022-00905-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13634-022-00905-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-022-00905-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T12:47:14Z","timestamp":1728046034000},"score":1,"resource":{"primary":{"URL":"https:\/\/asp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13634-022-00905-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,22]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["905"],"URL":"https:\/\/doi.org\/10.1186\/s13634-022-00905-z","relation":{},"ISSN":["1687-6180"],"issn-type":[{"type":"electronic","value":"1687-6180"}],"subject":[],"published":{"date-parts":[[2022,9,22]]},"assertion":[{"value":"12 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All experiments are the computer simulations that used the acoustical databases, and do not relate with human and animals in this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"88"}}