{"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\/news\/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":"

Azienda Google ha pubblicato<\/a><\/noindex> un database di suoni misti di riferimento, fornito di annotazioni, che pu\u00f2 essere utilizzato in sistemi di apprendimento automatico, applicati alla separazione di suoni misti casuali in componenti distinti. \u00c8 stata pubblicata anche una modellazione universale di deep learning (TDCN++), che pu\u00f2 essere utilizzata in Tensorflow per la separazione dei suoni. I dati sono stati preparati sulla base di una collezione 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 casuali, il cui carattere non \u00e8 noto in anticipo. Altri sistemi simili tendono a essere limitati alla separazione di suoni specifici, come la voce e i suoni non vocali o di diverse persone che parlano. <\/p>\n

Il database conta circa 20.000 mescolamenti. Il set include anche le caratteristiche impulsive della stanza pre-calcolate, preparate utilizzando uno speciale simulatore di camere che tiene conto della riflessione dalle pareti, della posizione della sorgente sonora e della 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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