走在汽車革命的路上論文書籍站
Shazam的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列包括價格和評價等資訊懶人包
Shazam的問題，我們搜遍了碩博士論文和台灣出版的書籍，推薦Foxx, Jamie寫的 ACT Like You Got Some Sense: And Other Things My Daughters Taught Me 和Berrios, Frank的 Heroes Help! (DC Shazam!): Featuring Black Adam!都 可以從中找到所需的評價。
另外網站蘋果併購Shazam 後！現在全球月活躍用戶突破2 億人 - 愛瘋日報也說明：美國Apple 公司在2018 年併購了音樂識別服務Shazam，並已將其功能整合在iOS 系統中，全球月活躍用戶已突破2 億。Shazam 可作為獨立App 應用， ...
這兩本書分別來自 和所出版 。
國立中央大學 通訊工程學系 張寶基所指導 李欣成的 基於動態時間規整結合線性伸縮之哼唱檢索系統 （2020），提出Shazam關鍵因素是什麼，來自於音樂資訊檢索、哼唱檢索、動態時間規整、線性伸縮。
而第二篇論文國立臺灣大學 資訊工程學研究所 張智星所指導 陳羿豐的 改善基於神經網路與地標法的音訊指紋 （2020），提出因為有 音樂檢索、音訊指紋、地標法、對比學習、二階段洗牌、資料擴增、支援向量機的重點而找出了 Shazam的解答。
最後網站兩段片尾、彩蛋角色及沙贊未來如何與DCEU連結 - 電影神搜則補充：(Shazam! ) 終於上映（台灣3/29-31 口碑場4/3 正式上映），帶來一部截然不同風格的歡樂向英雄喜劇電影。在這部全新的DC 英雄個人電影上映後， ...
ACT Like You Got Some Sense: And Other Things My Daughters Taught Me
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為了解決Shazam 的問題，作者Foxx, Jamie 這樣論述：
From Academy Award-winning multi-talent Jamie Foxx, a hilariously candid look at the joys and pitfalls of being the father of two daughters, now in paperback. Jamie Foxx is not only an actor, comedian, and musician, he’s also starring in his most humbling and long-running role yet as father to two i
ndependent girls: Corinne and Anelise. While his daughters have very different views on the world, there is one thing they can agree on: Dad gets on their motherf***ing nerves. Though every day with his girls brings hurdles and hilarity, he’s learned a lot along the way. In ACT LIKE YOU GOT SOME SEN
SE--a title inspired by his beloved and fierce grandmother--Jamie reveals his rocky parenting journey through priceless stories about the tough love and old-school values he learned growing up in the small town of Terrell, Texas; his early days trying to make it in Hollywood; and life after achievin
g stardom. You would think being an A-lister would ease his dad-duty struggles, but if anything, it has only made things more complicated. It seems that a teenage girl who just wants to blend in with her friends will not be excited to see her dad’s flashy new convertible at the front of the carpool
lane. Hilarious, poignant, and always brutally honest, ACT LIKE YOU GOT SOME SENSE is Jamie Foxx like we’ve never seen him before, dealing with problems he never imagined he’d have. Jamie Foxx is an award-winning actor, singer, and comedian. His accolades include an Academy Award for Best Actor,
a BAFTA Award for Best Actor in a Leading Role, a Golden Globe Award for Best Actor in a Motion Picture - Musical or Comedy, and a SAG Award for Outstanding Performance by a Male Actor in a Leading Role. He’s also a Grammy Award-winning musician, having produced four albums that charted in the top t
en in the United States. He has two daughters, Corinne and Anelise, and currently executive produces and hosts ＂Beat Shazam＂ on Fox with the former.
Shazam進入發燒排行的影片
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基於動態時間規整結合線性伸縮之哼唱檢索系統
為了解決Shazam 的問題，作者李欣成 這樣論述：
隨著智慧型手機以及行動網路的普及，使用音樂串流平台或社群網站搜索、下載音樂資料成為日常生活的一部分。對於腦海中有某首歌的旋律卻想不出歌名、歌詞的情況，內涵式音樂檢索系統(Content Based Music Retrieval, CBMR)如哼唱檢索系統就可直接使用歌曲內涵式特徵如旋律、節奏等做為檢索依據，解決上述問題。對於較大的檢索資料庫，我們必須在辨識準確率與運算時間中做權衡，先前研究以線性伸縮(Linear Scaling, LS)或推土機距離(Earth Mover’s distance, EMD)等準確度較低但運算速度快的方法先過濾掉不相似歌曲，再以高複雜度的動態時間規整(Dyn
amic Time Warping, DTW)對剩餘歌曲做高精度比對，最後做相似度融合並輸出前十相似的歌曲清單。本研究將LS用在縮短旋律特徵以及在優化模塊中當作微調特徵長度的工具，前者可以減少運算量後者可以使準確度再上升，高精度的DTW則負責計算匹配距離，在歌曲中移動及伸縮匹配窗口找出對應的旋律起始點以及最佳匹配距離，並設計優化模塊及前置過濾器。實驗結果顯示，本系統的MRR值優於先前的研究，線性縮短資料與優化閥值節省了約40%運算時間。
Heroes Help! (DC Shazam!): Featuring Black Adam!
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為了解決Shazam 的問題，作者Berrios, Frank 這樣論述：
Shazam! and Black Adam team up in this Little Golden Book starring the beloved DC characters!Even the smallest DC Super Hero fans can learn all about teaming up to lend a hand in this Little Golden Book starring Shazam! and Black Adam. The hero and villain are brought together to solve a problem, an
d in the process, discover what they have in common. This Little Golden Book is perfect for children ages 2 to 5, as well as fans and collectors of all ages! Little Golden Books enjoy nearly 100% consumer recognition. They feature beloved classics, hot licenses, and new original stories . . . the cl
assics of tomorrow.
改善基於神經網路與地標法的音訊指紋
為了解決Shazam 的問題，作者陳羿豐 這樣論述：
音訊指紋是一種音樂檢索方式，可用來快速的從錄音中辨識出相符的音樂，其作法是從錄音檔抽取顯著的特徵，並將此特徵和資料庫中的音樂特徵做比對。由於錄音經常會受到雜訊干擾，因此音訊指紋需要有抵抗環境噪音的能力。過去音訊指紋的做法主要是傳統演算法，如Avery Wang提出的地標法，近年來基於深度學習的音訊指紋做法已逐漸成為主流，如Google提出的Now Playing。此篇研究主要聚焦在Sungkyun Chang等人提出的神經網路法音訊指紋。本論文首先以MIREX音訊指紋資料集來評估神經網路法和地標法，顯示出神經網路法在以現實世界的錄音來測試時，精準度仍然不如傳統演算法。因此本論文提出了三種方法
來改進神經網路法：二階段洗牌、資料擴增改良以及對查詢做多次時間位移，並在最後以支援向量機(Support Vector Machine, SVM)來整合地標法和神經網路法的結果。為了方便重現，實驗使用公開的Free Music Archive資料集，透過加入雜訊的方式生成查詢音檔，並依照雜訊的強度分別計算檢索精準度。實驗結果顯示本論文提出的改進方式能夠顯著的提升神經網路在強雜訊下的精準度，並使得神經網路法在現實世界錄音查詢的表現超越地標法。