\n<\/p>\n
This paper was accepted on the IEEE Workshop on Purposes of Sign Processing to Audio and Acoustics (WASPAA) 2025<\/p>\n
Non-negative Matrix Factorization (NMF) is a strong method for analyzing regularly-sampled information, i.e., information that may be saved in a matrix. For audio, this has led to quite a few purposes utilizing time-frequency (TF) representations just like the Quick-Time Fourier Rework. Nevertheless extending these purposes to irregularly-spaced TF representations, just like the Fixed-Q rework, wavelets, or sinusoidal evaluation fashions, has not been doable since these representations can’t be instantly saved in matrix kind. On this paper, we formulate NMF by way of learnable capabilities (as a substitute of vectors) and present that NMF may be prolonged to a greater diversity of sign lessons that needn’t be frequently sampled.<\/p>\n
This paper was accepted on the IEEE Workshop on Purposes of Sign Processing to Audio and Acoustics (WASPAA) 2025 Non-negative Matrix Factorization (NMF) is a strong method for analyzing regularly-sampled information, i.e., information that may be saved in a matrix. For audio, this has led to quite a few purposes utilizing time-frequency (TF) representations just […]<\/p>\n","protected":false},"author":2,"featured_media":6353,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[55],"tags":[5176,5177,5175,298,5174,5178,5173],"class_list":["post-6351","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-factorization","tag-implicit","tag-matrix","tag-neural","tag-nonnegative","tag-representations","tag-rethinking"],"_links":{"self":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/6351","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6351"}],"version-history":[{"count":1,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/6351\/revisions"}],"predecessor-version":[{"id":6352,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/6351\/revisions\/6352"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/media\/6353"}],"wp:attachment":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6351"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6351"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6351"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}