{"id":24391863,"url":"https://github.com/teradepth/iva","last_synced_at":"2025-12-24T17:31:59.117Z","repository":{"id":201409837,"uuid":"121134319","full_name":"teradepth/iva","owner":"teradepth","description":"IVA: Independent Vector Analysis implementation","archived":false,"fork":false,"pushed_at":"2018-02-11T15:31:08.000Z","size":5,"stargazers_count":57,"open_issues_count":0,"forks_count":35,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-01-19T17:46:34.103Z","etag":null,"topics":["blind-source-separation","independent-component-analysis","independent-vector-analysis","unsupervised-learning"],"latest_commit_sha":null,"homepage":"","language":"Matlab","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/teradepth.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2018-02-11T14:57:52.000Z","updated_at":"2024-12-03T07:42:12.000Z","dependencies_parsed_at":null,"dependency_job_id":"9d0e2b28-e89a-41ae-90b1-836dd536d4e9","html_url":"https://github.com/teradepth/iva","commit_stats":null,"previous_names":["teradepth/iva"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/teradepth%2Fiva","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/teradepth%2Fiva/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/teradepth%2Fiva/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/teradepth%2Fiva/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/teradepth","download_url":"https://codeload.github.com/teradepth/iva/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243318748,"owners_count":20272137,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["blind-source-separation","independent-component-analysis","independent-vector-analysis","unsupervised-learning"],"created_at":"2025-01-19T17:42:59.990Z","updated_at":"2025-12-24T17:31:59.063Z","avatar_url":"https://github.com/teradepth.png","language":"Matlab","funding_links":[],"categories":[],"sub_categories":[],"readme":"# IVA: Independent Vector Analysis\n\n## matlab implementations\nivabss.m\n \n Natural Gradient algorithm for Frecuency Domain Blind source separation based on Independent Vector Analysis\n        \n    [y, W] = ivabss(x, nfft, maxiter, tol, eta, nsou)\n     y : separated signals (nsou x N)\n     W : unmixing matrices (nsou x nmic x nfft/2+1)\n     x : observation signals (nmic x N),\n           where nsou is # of sources, nmic is # of mics, and N is # of time frames\n     nfft : # of fft points (default =1024)\n     eta : learning rate (default =0.1)\n     maxiter : # of iterations (default =1000)\n     tol : When the difference of objective is less than tol,\n               the algorithm terminates (default =1e-6)\n     nsou : # of sources (default =nmic)\n     \nfiva.m\n\n  Fast algorithm for Frecuency Domain Blind source separation\n        based on Independent Vector Analysis\n\n    [y, W] = fivabss(x, nfft, maxiter, tol, nsou)\n     y : separated signals (nsou x N)\n     W : unmixing matrices (nsou x nmic x nfft/2+1)\n     x : observation signals (nmic x N),\n           where nsou is # of sources, nmic is # of mics, and N is # of time frames\n     nfft : # of fft points (default =1024)\n     maxiter : # of iterations (default =1000)\n     tol : When the increment of likelihood is less than tol,\n               the algorithm terminates (deault =1e-6)\n     nsou : # of sources (default =nmic)\n\n## python implementation\n\nTO-DO\n\n\n## References\n[1] Taesu Kim, \"Independent Vector Analysis\" Ph.D. Dissertation, KAIST, 2007\n\n[2] Taesu Kim, Hagai Attias, Soo-Young Lee, Te-Won Lee, \"Blind source separation exploiting higher-order frequency dependencies\" IEEE Transactions on Audio, Speech, and Language Processing 15 (1), 2007\n\n[3] Intae Lee, Taesu Kim, Te-Won Lee, \"Fast fixed-point independent vector analysis algorithms for convolutive blind source separation\" Signal Processing 87 (8), 2007\n\n[4] Taesu Kim, Torbjørn Eltoft, Te-Won Lee, \"Independent vector analysis: An extension of ICA to multivariate components\" International Conference on Independent Component Analysis and Signal Separation, 2006\n\n[5] Taesu Kim, Intae Lee, Te-Won Lee, \"Independent vector analysis: definition and algorithms\", Fortieth Asilomar Conference on Signals, Systems and Computers, 2006\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fteradepth%2Fiva","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fteradepth%2Fiva","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fteradepth%2Fiva/lists"}