{"id":77570,"date":"2020-04-11T19:42:07","date_gmt":"2020-04-11T17:42:07","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/google-opublikoval-dannye-i-model-mashinnogo-obucheniya-dlya-razdeleniya-zvukov"},"modified":"2020-04-11T19:42:07","modified_gmt":"2020-04-11T17:42:07","slug":"google-opublikoval-dannye-i-model-mashinnogo-obucheniya-dlya-razdeleniya-zvukov","status":"publish","type":"post","link":"https:\/\/prohoster.info\/et\/blog\/news\/google-opublikoval-dannye-i-model-mashinnogo-obucheniya-dlya-razdeleniya-zvukov","title":{"rendered":"Google avaldas andmed ja masin\u00f5ppe mudeli helide eraldamiseks","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Google avalikustasin<\/a><\/noindex> oodatud segatud helide andmebaas, varustatud annotatsioonidega, mida saab kasutada masin\u00f5ppes\u00fcsteemides, et jagada juhuslikke segatud helisid eraldi komponentideks. Samuti on avaldatud universaalne s\u00fcgava masin\u00f5ppe mudel (TDCN++), mida saab kasutada Tensorflow's helide eraldamiseks. Andmed on ette valmistatud kogumite p\u00f5hjal freesound.org<\/a><\/noindex> ja avaldatud<\/a><\/noindex> CC BY 4.0 litsentsi all. <\/p>\n

Esitatud projekt FUSS (Free Universal Sound Separation) on suunatud juhuslike helide eraldamise probleemile, mille iseloom ei ole eelnevalt teada. Teised sarnased s\u00fcsteemid on tavaliselt piiratud teatud helide eraldamise \u00fclesanne, n\u00e4iteks r\u00e4\u00e4kijate ja mitte-r\u00e4\u00e4kijate v\u00f5i erinevate r\u00e4\u00e4kijate vahel. <\/p>\n

Andmebaasis on umbes 20 000 segamist. Komplekti kuuluvad ka eelnevalt arvutatud ruumi impulsskarakteristikud, mis on valmistatud spetsiaalselt loodud ruumisimulaatori abil ja arvestavad seinte peegeldust, heliallika asukohta ja mikrofoni asukohta. <\/p>\n

<\/a><\/noindex><\/p>\n

Allikas: opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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