{"id":48579,"date":"2026-09-30T03:08:52","date_gmt":"2026-09-30T00:08:52","guid":{"rendered":"https:\/\/stacc.ee\/?p=48579"},"modified":"2026-09-30T03:31:10","modified_gmt":"2026-09-30T00:31:10","slug":"kuidas-ai-aitab-kuulmislangusega-inimestel-tuvastada-koduseid-helisid","status":"publish","type":"post","link":"https:\/\/stacc.ee\/et\/kuidas-ai-aitab-kuulmislangusega-inimestel-tuvastada-koduseid-helisid\/","title":{"rendered":"Kuidas AI aitab kuulmislangusega inimestel tuvastada koduseid helisid"},"content":{"rendered":"\n

<\/p>\n\n\n\n

Kuulmislangusega inimese jaoks v\u00f5ib suitsuanduri h\u00e4\u00e4l v\u00f5i uksekella helin olla eluliselt t\u00e4htis info. Just seet\u00f5ttu arendab Eesti ettev\u00f5te Soundfree<\/a> koost\u00f6\u00f6s STACCiga tehisintellektil p\u00f5hinevat lahendust, mis aitab tuvastada olulisi koduseid helisid.<\/p>\n\n\n\n

STACCi andmeteadlane Johanna Kaare t\u00f6\u00f6tas v\u00e4lja esialgse algoritmi, mis suudab kasutaja salvestatud heliklipist automaatselt eraldada olulise signaali, v\u00f5imaldades s\u00fcsteemil \u00f5ppida tuvastama just kasutaja kodus k\u00f5lavaid helisid. T\u00e4naseks on lahendus arenenud edasi: uus arendusetapp lisas s\u00fcsteemile v\u00f5imekuse hinnata heliklipi kvaliteeti ning mudeli enesekindlust tehtud tuvastuses. See t\u00e4hendab, et s\u00fcsteem mitte ainult ei \u00fctle, mis heli see on, vaid ka seda, kui usaldusv\u00e4\u00e4rne see hinnang on.<\/p>\n\n\n\n

Lahendus, mis kohandub iga kodu j\u00e4rgi <\/strong><\/h3>\n\n\n\n

Iga kodu helid on erinevad. \u00dchel piiksub ahi vaikselt, teisel k\u00f5lab uksekell hoopis madalama tooniga. Just seet\u00f5ttu arendab Soundfree s\u00fcsteemi, mis v\u00f5imaldab kasutajatel \u00f5petada tehisintellekti \u00e4ra tundma nende kodule omaseid helisid.<\/p>\n\n\n\n

Selleks tuleb kasutajal salvestada heliklipp soovitud heliga \u2013 n\u00e4iteks uksekella v\u00f5i mikrolaineahju piiksuga. STACCi loodud algoritm l\u00f5ikab heliklipist automaatselt v\u00e4lja olulise signaali, j\u00e4ttes k\u00f5rvale taustam\u00fcra. See muudab s\u00fcsteemi ligip\u00e4\u00e4setavaks ka kuulmislangusega inimestele, kellel pole v\u00f5imalik salvestust kuulata ega seda ise t\u00f6\u00f6delda.<\/p>\n\n\n\n

S\u00fcsteem kasutab olulise helisignaali tuvastamiseks mitut teineteist t\u00e4iendavat anal\u00fc\u00fcsimeetodit, mis aitavad eristada sihtheli ka taustam\u00fcra korral. Need valiti varasema t\u00f6\u00f6 k\u00e4igus<\/a> loodud kuue algoritmi seast. Valjut ja selget signaali (n\u00e4iteks suitsuanduri alarmi) on \u00fcldjuhul lihtsam taustast eristada, kuid vaiksema heli v\u00f5i tugeva taustam\u00fcra korral on \u00fclesanne keerulisem. Mitme anal\u00fc\u00fcsimeetodi kasutamine aitab s\u00fcsteemil toime tulla erinevate helide ja salvestustingimustega.<\/p>\n\n\n\n

Kuidas hinnata, kas salvestus on kvaliteetne?<\/strong><\/h3>\n\n\n\n

Praktiline v\u00e4ljakutse seisneb selles, et kodus salvestatud heliklipid ei pruugi alati olla sobiva kvaliteediga. V\u00f5ib juhtuda, et heli on liiga vaikne v\u00f5i segatud muude koduste helidega \u2013 n\u00e4iteks v\u00f5ib uksekella salvestuse vahele j\u00e4\u00e4da koputus v\u00f5i kiirabi sireen.<\/p>\n\n\n\n

Et selliseid segadusi v\u00e4ltida, loodi s\u00fcsteem, mis hindab automaatselt helifailide kvaliteeti. S\u00fcsteem anal\u00fc\u00fcsib sama klassi helisalvestiste omavahelist sarnasust. Kui m\u00f5ni salvestis erineb \u00fclej\u00e4\u00e4nutest m\u00e4rgatavalt, v\u00f5ib see viidata sellele, et treeningandmestikku on sattunud vale heli v\u00f5i kvaliteedilt sobimatu salvestis.<\/p>\n\n\n\n

Mudel, mis oskab hinnata enda kindlust<\/strong><\/h3>\n\n\n\n

Teine oluline uuendus on mudeli enesekindluse hindamine. Kui s\u00fcsteem tuvastab heli (n\u00e4iteks uksekella), annab ta ka hinnangu, kui veendunud ta selles tuvastuses on. Enesekindlus v\u00e4ljendub arvulise skoorina, mis n\u00e4itab, kui kindel mudel oma ennustuses on. Mida k\u00f5rgem on skoor, seda suurema kindlusega peab mudel oma hinnangut usaldusv\u00e4\u00e4rseks.<\/p>\n\n\n\n

Mida see kasutajale annab? Kui enesekindlus on madal, v\u00f5ib kasutajatugi soovitada rohkem salvestusi lisada v\u00f5i mudelit t\u00e4iendavalt treenida. Kuigi hetkel ei kuvata neid skooritulemusi kasutajaliideses, logitakse need s\u00fcsteemis, et tuvastada probleemseid juhtumeid.<\/p>\n\n\n\n

Mudeli usaldusv\u00e4\u00e4rsuse hindamiseks kasutatakse mitut erinevat hindamismeetodit. Nende abil saab kontrollida, kui j\u00e4rjepidevalt mudel helisid tuvastab, ning avastada olukordi, kus mudeli hinnang v\u00f5ib vajada t\u00e4iendavat t\u00e4helepanu. See aitab tagada, et s\u00fcsteemi otsused oleksid v\u00f5imalikult usaldusv\u00e4\u00e4rsed ka p\u00e4riselu olukordades.<\/p>\n\n\n\n

Tehniline v\u00e4ljakutse: automaatika, mis ei j\u00e4\u00e4 toppama<\/strong><\/h3>\n\n\n\n

Kuigi protsess k\u00f5lab keeruliselt \u2013 heli puhastamine, l\u00f5ikamine, hindamine ja mudeli treenimine \u2013 peab see k\u00f5ik toimuma kiirelt. Eriti kui soovime, et kasutajakogemus oleks sujuv.<\/p>\n\n\n\n

Selle saavutamine oligi j\u00e4tkuprojekti suurim tehniline proovikivi. T\u00e4naseks suudab s\u00fcsteem uue helin\u00e4idise t\u00f6\u00f6delda v\u00e4hem kui sekundiga ja personaalse mudeli treenimine v\u00f5tab alla minuti. Samas j\u00e4i ka arvutusressursside kulu oodatust oluliselt v\u00e4iksemaks.<\/p>\n\n\n\n

Et k\u00f5ik toimuks reaalajas ja minimaalse viivitusega, disainiti t\u00f6\u00f6voog nii, et heliklipi puhastamine, l\u00f5ikamine ja hindamine toimuvad j\u00e4rjestikku ning optimeeritud j\u00e4rjekorras.<\/p>\n\n\n\n

Tehisintellekt, mis t\u00f5stab elukvaliteeti<\/strong><\/h3>\n\n\n\n

Kogu t\u00f6\u00f6 l\u00f5ppeesm\u00e4rk on \u00fcks: pakkuda kuulmislangusega inimestele turvalisemat ja sujuvamat igap\u00e4evaelu. Kui mudel suudab usaldusv\u00e4\u00e4rselt \u00e4ra tunda suitsuanduri heli v\u00f5i teavitada ahju piiksust, v\u00e4heneb eluohtlike olukordade risk ja paraneb elukvaliteet.<\/p>\n\n\n\n

STACCi andmeteadlane Johanna Kaare \u00fctleb, et t\u00f6\u00f6 selle lahendusega j\u00e4tkub, kuid senised tulemused on paljut\u00f5otavad. \u201cT\u00e4naseks oleme suutnud viia \u00fche isikup\u00e4rastatud helimudeli treenimise aja alla minuti \u2013 ja seda oodatust kordades v\u00e4iksema kuluga,\u201d s\u00f5nab ta. \u201cSee n\u00e4itab, et ka tehniliselt keerukas lahendus saab olla kiire, t\u00f5hus ja kasutajale mugav.\u201d<\/p>\n\n\n\n

Kui otsid partnerit, kes oskab keerulisest ideest luua t\u00f6\u00f6tava ja kasutajas\u00f5braliku andmelahenduse, siis v\u00f5ta meiega \u00fchendust! STACCi andmeteadlased aitavad sul viia visiooni ellu viisil, mis on tehniliselt l\u00e4bim\u00f5eldud ja praktiliselt kasutatav.<\/p>\n","protected":false},"excerpt":{"rendered":"

Kuulmislangusega inimese jaoks v\u00f5ib suitsuanduri h\u00e4\u00e4l v\u00f5i uksekella helin olla eluliselt t\u00e4htis info. Just seet\u00f5ttu arendab Eesti ettev\u00f5te Soundfree koost\u00f6\u00f6s STACCiga tehisintellektil p\u00f5hinevat lahendust, mis aitab tuvastada olulisi koduseid helisid. STACCi andmeteadlane Johanna Kaare t\u00f6\u00f6tas v\u00e4lja esialgse algoritmi, mis suudab kasutaja salvestatud heliklipist automaatselt eraldada olulise signaali, v\u00f5imaldades s\u00fcsteemil \u00f5ppida tuvastama just kasutaja kodus k\u00f5lavaid<\/p>\n","protected":false},"author":17,"featured_media":48580,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156],"tags":[],"class_list":["post-48579","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-assistendid"],"yoast_head":"\nKuidas AI aitab kuulmislangusega inimestel tuvastada koduseid helisid<\/title>\n<meta name=\"description\" content=\"Soundfree ja STACC arendavad AI-lahendust, mis \u00f5pib tundma iga kodu helisid ning aitab muuta oluliste helide tuvastamise usaldusv\u00e4\u00e4rsemaks ja kasutajas\u00f5bralikumaks.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/stacc.ee\/et\/kuidas-ai-aitab-kuulmislangusega-inimestel-tuvastada-koduseid-helisid\/\" \/>\n<meta property=\"og:locale\" content=\"et_EE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Kuidas AI aitab kuulmislangusega inimestel tuvastada koduseid helisid\" \/>\n<meta property=\"og:description\" content=\"Soundfree ja STACC arendavad AI-lahendust, mis \u00f5pib tundma iga kodu helisid ning aitab muuta oluliste helide tuvastamise usaldusv\u00e4\u00e4rsemaks ja kasutajas\u00f5bralikumaks.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/stacc.ee\/et\/kuidas-ai-aitab-kuulmislangusega-inimestel-tuvastada-koduseid-helisid\/\" \/>\n<meta property=\"og:site_name\" content=\"STACC\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/staccest\" \/>\n<meta property=\"article:published_time\" content=\"2026-09-30T00:08:52+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-09-30T00:31:10+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/stacc.ee\/wp-content\/uploads\/2025\/10\/soundfree.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1773\" \/>\n\t<meta property=\"og:image:height\" content=\"998\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Annet Muru\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"Kuidas AI aitab kuulmislangusega inimestel tuvastada koduseid helisid\" \/>\n<meta name=\"twitter:description\" content=\"Kuulmislangusega inimese jaoks v\u00f5ib suitsuanduri h\u00e4\u00e4l v\u00f5i uksekella helin olla eluliselt t\u00e4htis info. 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