{"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\/it\/blog\/novosti-interneta\/google-opublikoval-dannye-i-model-mashinnogo-obucheniya-dlya-razdeleniya-zvukov","title":{"rendered":"Google ha pubblicato dati e un modello di apprendimento automatico per la separazione dei suoni.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Google ha pubblicato<\/a><\/noindex> un database di suoni misti standard, fornito di annotazioni, utilizzabile in sistemi di apprendimento automatico per la separazione di suoni misti arbitrari in singoli componenti. \u00c8 stata inoltre pubblicata un modello universale di apprendimento profondo (TDCN++), utilizzabile in Tensorflow per la separazione dei suoni. I dati sono stati preparati sulla base di una raccolta freesound.org<\/a><\/noindex> e pubblicati<\/a><\/noindex> con licenza CC BY 4.0. <\/p>\n

Il progetto presentato FUSS (Free Universal Sound Separation) mira a risolvere il problema della separazione di un numero qualsiasi di suoni arbitrari, il cui carattere non \u00e8 noto in anticipo. Altri sistemi simili tendono a limitarsi alla separazione di suoni specifici, ad esempio, voce e non voce o diversi parlanti. <\/p>\n

Il DB conta circa 20.000 mescolanze. Il set include anche le caratteristiche impulsive calcolate in anticipo dell'ambiente, preparate utilizzando un simulatore di camera appositamente creato e considerando il riflesso dalle pareti, la posizione della sorgente sonora e la posizione del microfono. <\/p>\n

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

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

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