K\u00fct\u00fcphane E\u015fle\u015fmesi:<\/strong> Sunucular, olu\u015fturulan \u00f6zg\u00fcn kodlar\u0131 k\u00fct\u00fcphanedeki milyonlarca kay\u0131tla k\u0131yaslar. Bu s\u00fcre\u00e7te sanat\u00e7\u0131 ve eser bilgilerine en do\u011fru \u015fekilde ula\u015fman\u0131z\u0131 sa\u011flar.<\/li>\n<\/ol>\nAnaliz s\u00fcreci, zaman i\u00e7indeki frekans de\u011fi\u015fimlerini sabit bir veri setine indirger. B\u00f6ylece \u00e7evresel seslerin yaratt\u0131\u011f\u0131 karma\u015fay\u0131 ortadan kald\u0131r\u0131r. Ayr\u0131ca videodan \u015fark\u0131 bulma<\/strong> sitesi, en zay\u0131f sinyalleri dahi y\u00fcksek do\u011fruluk oran\u0131na sahip olan dijital imzalara d\u00f6n\u00fc\u015ft\u00fcr\u00fcr.<\/p>\n<\/span>Landmark ve Hash Noktalar\u0131: \u015eark\u0131lar\u0131n E\u015fle\u015fme S\u00fcreci<\/span><\/h2>\nShazam, ses spektrumundaki en bask\u0131n frekans zirvelerini belirler. Bu stratejik nirengi noktalar\u0131n\u0131 landmark<\/strong> olarak tan\u0131mlar. Algoritma, bu zirvelerin birbirlerine olan uzakl\u0131\u011f\u0131n\u0131 ve zaman fark\u0131n\u0131 \u00f6l\u00e7\u00fcp \u015fark\u0131n\u0131n melodik iskeletini \u00e7\u0131kar\u0131r. Ayr\u0131ca yaz\u0131l\u0131m, g\u00fcr\u00fclt\u00fcl\u00fc ortamlarda dahi yaln\u0131zca y\u00fcksek enerjili noktalar\u0131 dikkate al\u0131r ve \u00e7evresel sesleri analiz d\u0131\u015f\u0131nda tutar.<\/p>\nSistem, belirlenen landmark \u00e7iftlerini hash<\/strong> ad\u0131 verilen k\u0131sa ve \u00f6zg\u00fcn matematiksel kodlara d\u00f6n\u00fc\u015ft\u00fcr\u00fcr. Bu noktada her bir hash kodu, \u015fark\u0131n\u0131n belirli bir an\u0131ndaki frekans ili\u015fkisini temsil eden dijital m\u00fch\u00fcr i\u015flevi g\u00f6r\u00fcr. S\u00f6z konusu i\u015flem, b\u00fcy\u00fck ses dosyalar\u0131n\u0131 k\u00fc\u00e7\u00fck ve taranabilir veri paketlerine indirgeyip s\u00fcreci h\u0131zland\u0131r\u0131r. Yaz\u0131l\u0131m, bu hash kodlar\u0131n\u0131 k\u00fct\u00fcphanedeki verilerle k\u0131yaslar. Bu s\u0131rada hem ses benzerli\u011fine hem de verilerin zaman ak\u0131\u015f\u0131ndaki dizilim s\u0131ras\u0131na odaklan\u0131r.<\/p>\nSunucular, olu\u015fturulan bu \u00f6zg\u00fcn hash dizilerini saniyeler i\u00e7inde k\u0131yaslar. Bu i\u015flem i\u00e7in k\u00fct\u00fcphanedeki milyonlarca kay\u0131tl\u0131 parmak izini kullan\u0131r. Uygulama, e\u015fle\u015fme ger\u00e7ekle\u015fti\u011fi anda matematiksel verileri birle\u015ftirir. Bu verileri somut bir sanat\u00e7\u0131 ve eser ismiyle ili\u015fkilendirip sonucu ekrana yans\u0131t\u0131r. Verileri s\u00fczerek i\u015fleyen teknoloji, en zay\u0131f sinyallerde bile etkili sonu\u00e7lar verir. Bu sayede hatas\u0131z bir tan\u0131mlama performans\u0131 sergiler.<\/p>\n
![\"Ak\u0131ll\u0131](\"https:\/\/sosyalevin.com\/blog\/wp-content\/uploads\/2026\/04\/gurultulu-ortamlarda-shazam-nasil-calisir.webp\" "\\"\\"")
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<\/span>G\u00fcr\u00fclt\u00fcl\u00fc Ortamlarda Shazam Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/span><\/h2>\nUygulama, ses sinyallerini frekans ve zaman ekseninde analiz eder. Bu teknolojiyle, spektrogram ad\u0131 verilen \u00fc\u00e7 boyutlu veri haritalar\u0131 olu\u015fturur. Bu kapsamda algoritma, g\u00fcr\u00fclt\u00fcl\u00fc ortamlarda bile \u015fark\u0131lar\u0131 hatas\u0131z saptamak i\u00e7in a\u015fa\u011f\u0131daki ad\u0131mlar\u0131 takip eder:<\/p>\n
\n- Sinyal Analizi:<\/strong> Sistem, mikrofon arac\u0131l\u0131\u011f\u0131yla al\u0131nan ses karma\u015fas\u0131n\u0131 frekanslar\u0131na ay\u0131r\u0131r. D\u00fc\u015f\u00fck enerjili arka plan u\u011fultular\u0131n\u0131 ise matematiksel filtrelemeyle eler.<\/li>\n
- Spektral Zirvelerin Belirlenmesi:<\/strong> Algoritma, spektrogram \u00fczerindeki en y\u00fcksek genli\u011fe sahip noktalar\u0131 se\u00e7er. Sadece spektral zirvelere odaklanarak aranan \u015fark\u0131n\u0131n melodik iskeletini temsil eden bir y\u0131ld\u0131z haritas\u0131 olu\u015fturur.<\/li>\n
- Hash Koduna D\u00f6n\u00fc\u015ft\u00fcrme:<\/strong> Yaz\u0131l\u0131m, belirlenen bu enerji noktalar\u0131 aras\u0131ndaki zaman farklar\u0131n\u0131 \u00f6l\u00e7\u00fcp hash kodlar\u0131na d\u00f6n\u00fc\u015ft\u00fcr\u00fcr. Bu i\u015flemle sesin g\u00fcr\u00fclt\u00fcden etkilenmeyen dijital parmak izinin haz\u0131rlanmas\u0131n\u0131 sa\u011flar.<\/li>\n
- Veri Taban\u0131 K\u0131yaslamas\u0131:<\/strong> Uygulama, olu\u015fturulan bu \u00f6zg\u00fcn kodlar\u0131 saniyeler i\u00e7inde k\u0131yaslar. Bulut tabanl\u0131 devasa k\u00fct\u00fcphanedeki milyonlarca kayd\u0131 tarayarak e\u015fle\u015fme olas\u0131l\u0131klar\u0131n\u0131 titizlikle analiz eder.<\/li>\n
- Bilgi E\u015fle\u015fmesi:<\/strong> Sunucular, e\u015fle\u015fme ger\u00e7ekle\u015fti\u011fi anda devreye girip soyut verileri somut eser ismi ve sanat\u00e7\u0131 bilgisine d\u00f6n\u00fc\u015ft\u00fcrerek sonucu ekrana yans\u0131t\u0131r.<\/li>\n<\/ol>\n
Analiz s\u00fcreci, sesin tamam\u0131n\u0131 kaydetmekle u\u011fra\u015fmaz. Bunun yerine yaln\u0131zca stratejik frekans zirvelerine odaklan\u0131r. B\u00f6ylece \u00e7evresel seslerin yaratt\u0131\u011f\u0131 kirlilik, veri setinin d\u0131\u015f\u0131nda kal\u0131r. Modelleme, m\u00fczi\u011fin \u00f6zg\u00fcn karakterini en kalabal\u0131k mekanlarda bile korur. Milisaniyelik h\u0131zlarla \u00e7al\u0131\u015farak do\u011fru sonu\u00e7lara ula\u015f\u0131lmas\u0131n\u0131 sa\u011flar.<\/p>\n
<\/span>SoundHound vs Shazam<\/span><\/h2>\nShazam ve SoundHound, m\u00fczik tan\u0131ma i\u015flemini farkl\u0131 teknolojik yakla\u015f\u0131mlar arac\u0131l\u0131\u011f\u0131yla ger\u00e7ekle\u015ftirir. Bu iki uygulama, m\u00fczik tan\u0131ma konusunda olduk\u00e7a pop\u00fclerdir. Bu noktada Shazam, orijinal kay\u0131tlar\u0131 saptama h\u0131z\u0131yla bilinir. SoundHound Online<\/strong> ise kullan\u0131c\u0131 sesinden melodi tan\u0131ma yetene\u011fiyle \u00f6ne \u00e7\u0131kar. \u0130ki platform aras\u0131ndaki temel farklar a\u015fa\u011f\u0131daki gibi \u00f6zetlenebilir:<\/p>\n\n- Shazam, orijinal \u015fark\u0131 kay\u0131tlar\u0131n\u0131 saniyeler i\u00e7inde k\u00fct\u00fcphanesindeki verilerle e\u015fle\u015ftirip kullan\u0131c\u0131ya sunar. \u00c7\u00fcnk\u00fc bu uygulama, Apple ekosistemiyle uyumludur.<\/li>\n
- SoundHound, kullan\u0131c\u0131lar melodiyi \u0131sl\u0131kla \u00e7ald\u0131\u011f\u0131nda veya m\u0131r\u0131ldand\u0131\u011f\u0131nda bile do\u011fru sonu\u00e7lar \u00fcretir. \u015eark\u0131 bulma y\u00f6ntemini bu \u015fekilde kullananlar i\u00e7in etkili \u00e7\u00f6z\u00fcmler sunar.<\/li>\n
- Shazam, aray\u00fcz tasar\u0131m\u0131yla \u00fcyelerine kolayl\u0131k sa\u011flar. Aranan par\u00e7alar\u0131 do\u011frudan \u00e7alma listelerine aktarma ve \u015fark\u0131 s\u00f6zlerini anl\u0131k takip etme f\u0131rsat\u0131 sunar.<\/li>\n
- SoundHound uygulamas\u0131nda yerle\u015fik bir asistan bulunur. Bu asistan, kullan\u0131c\u0131lar\u0131n ellerini kullanmadan arama yapmas\u0131n\u0131 sa\u011flar.<\/li>\n
- Shazam\u2019\u0131n filtreleme algoritmas\u0131, g\u00fcr\u00fclt\u00fcl\u00fc ortamlarda bile stabil \u00e7al\u0131\u015f\u0131r. Uygulama, m\u00fczi\u011fi karma\u015f\u0131k sesler aras\u0131ndan h\u0131zl\u0131 bir \u015fekilde ay\u0131klar.<\/li>\n<\/ul>\n
Kullan\u0131c\u0131lar, uygulama se\u00e7imlerini o anki ihtiya\u00e7lar\u0131na g\u00f6re ger\u00e7ekle\u015ftirmelidir. Bu s\u00fcre\u00e7te ak\u0131ll\u0131 cihazlar\u0131n\u0131n \u00f6zelliklerini de dikkate almalar\u0131 gerekir. Shazam, halihaz\u0131rda \u00e7alan orijinal bir kayd\u0131 bulmak isteyenler i\u00e7in idealdir. G\u00fcr\u00fclt\u00fcl\u00fc mekanlarda bile en y\u00fcksek h\u0131zda sonu\u00e7lar verir. Bu ba\u011flamda uygulama, Apple entegrasyonuyla g\u00fc\u00e7lendirilmi\u015ftir.<\/p>\n
SoundHound, akl\u0131nda kalan ezgiyi sesiyle canland\u0131rarak bulmay\u0131 hedefleyenler i\u00e7in uygundur. Uygulama, ses analizi i\u00e7in \u00f6zel algoritmalar kullan\u0131r. Bu algoritmalar sayesinde ses analizi konusunda uzmanla\u015fm\u0131\u015ft\u0131r. Sistem, \u0131sl\u0131k gibi farkl\u0131 ses giri\u015flerini bile orijinal kay\u0131tlar\u0131n ana frekans yap\u0131s\u0131yla e\u015fle\u015ftirir. Bu y\u00f6ntem sayesinde \u015fark\u0131y\u0131 bulma ihtimalini art\u0131r\u0131r.<\/p>\n\n\t\t
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Shazam \u015eark\u0131lar\u0131 Nas\u0131l Buluyor? <\/span><\/h3>\n\t\t\tSesin akustik parmak izini \u00e7\u0131kar\u0131r ve veri taban\u0131ndaki milyonlarca kay\u0131tla saniyeler i\u00e7inde e\u015fle\u015ftirir.\n\t\t\t<\/div>\n\t\t<\/div>\n\t\n\n\t\t
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Shazam Arka Planda Nas\u0131l \u00c7al\u0131\u015f\u0131r? <\/span><\/h3>\n\t\t\tAuto Shazam moduyla mikrofonu d\u00fc\u015f\u00fck g\u00fc\u00e7te kullan\u0131r ve ortamdaki m\u00fczikleri otomatik olarak listeler.\n\t\t\t<\/div>\n\t\t<\/div>\n\t\n\n\t\t
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Shazam Hangi \u00dclkenin? <\/span><\/h3>\n\t\t\t\u0130ngiltere’de kurulmu\u015ftur, g\u00fcn\u00fcm\u00fczde ise ABD merkezli Apple’a aittir.\n\t\t\t<\/div>\n\t\t<\/div>\n\t\n","protected":false},"excerpt":{"rendered":"
Shazam, ortamdaki ses dalgalar\u0131n\u0131 dijital verilere d\u00f6n\u00fc\u015ft\u00fcr\u00fcr. Bu teknoloji, her ses i\u00e7in benzersiz bir parmak izi olu\u015fturur. Yaz\u0131l\u0131m, ses sinyallerini frekans ve zaman ekseninde analiz eder. Ard\u0131ndan spektrogram denilen \u00fc\u00e7 boyutlu veri haritalar\u0131 olu\u015fturur. Bu s\u00fcre\u00e7te g\u00fcr\u00fclt\u00fcleri filtreler ve en karakteristik spektral zirveleri ay\u0131klar. Ard\u0131ndan \u015fark\u0131n\u0131n melodik iskeletini temsil eden bir y\u0131ld\u0131z haritas\u0131 \u00e7\u0131kar\u0131r. Shazam …<\/p>\n","protected":false},"author":9,"featured_media":7144,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-7140","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-genel"],"_links":{"self":[{"href":"https:\/\/sosyalevin.com\/blog\/wp-json\/wp\/v2\/posts\/7140","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sosyalevin.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sosyalevin.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sosyalevin.com\/blog\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/sosyalevin.com\/blog\/wp-json\/wp\/v2\/comments?post=7140"}],"version-history":[{"count":5,"href":"https:\/\/sosyalevin.com\/blog\/wp-json\/wp\/v2\/posts\/7140\/revisions"}],"predecessor-version":[{"id":7147,"href":"https:\/\/sosyalevin.com\/blog\/wp-json\/wp\/v2\/posts\/7140\/revisions\/7147"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sosyalevin.com\/blog\/wp-json\/wp\/v2\/media\/7144"}],"wp:attachment":[{"href":"https:\/\/sosyalevin.com\/blog\/wp-json\/wp\/v2\/media?parent=7140"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sosyalevin.com\/blog\/wp-json\/wp\/v2\/categories?post=7140"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sosyalevin.com\/blog\/wp-json\/wp\/v2\/tags?post=7140"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}