TW200307205A - Robust and invariant audio pattern matching - Google Patents

Robust and invariant audio pattern matching Download PDF

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TW200307205A
TW200307205A TW092109632A TW92109632A TW200307205A TW 200307205 A TW200307205 A TW 200307205A TW 092109632 A TW092109632 A TW 092109632A TW 92109632 A TW92109632 A TW 92109632A TW 200307205 A TW200307205 A TW 200307205A
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audio
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TW092109632A
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TWI269196B (en
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Avery Li-Chun Wang
Daniel Culbert
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Shazam Entertainment Ltd
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    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00Speaker identification or verification
    • G10L17/02Preprocessing operations, e.g. segment selection; Pattern representation or modelling, e.g. based on linear discriminant analysis [LDA] or principal components; Feature selection or extraction
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10HELECTROPHONIC MUSICAL INSTRUMENTS; INSTRUMENTS IN WHICH THE TONES ARE GENERATED BY ELECTROMECHANICAL MEANS OR ELECTRONIC GENERATORS, OR IN WHICH THE TONES ARE SYNTHESISED FROM A DATA STORE
    • G10H1/00Details of electrophonic musical instruments
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/02Feature extraction for speech recognition; Selection of recognition unit
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/08Speech classification or search
    • G10L15/10Speech classification or search using distance or distortion measures between unknown speech and reference templates
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00Speaker identification or verification
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/48Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
    • G10L25/51Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12Classification; Matching
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10HELECTROPHONIC MUSICAL INSTRUMENTS; INSTRUMENTS IN WHICH THE TONES ARE GENERATED BY ELECTROMECHANICAL MEANS OR ELECTRONIC GENERATORS, OR IN WHICH THE TONES ARE SYNTHESISED FROM A DATA STORE
    • G10H2240/00Data organisation or data communication aspects, specifically adapted for electrophonic musical tools or instruments
    • G10H2240/121Musical libraries, i.e. musical databases indexed by musical parameters, wavetables, indexing schemes using musical parameters, musical rule bases or knowledge bases, e.g. for automatic composing methods
    • G10H2240/131Library retrieval, i.e. searching a database or selecting a specific musical piece, segment, pattern, rule or parameter set
    • G10H2240/135Library retrieval index, i.e. using an indexing scheme to efficiently retrieve a music piece
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10HELECTROPHONIC MUSICAL INSTRUMENTS; INSTRUMENTS IN WHICH THE TONES ARE GENERATED BY ELECTROMECHANICAL MEANS OR ELECTRONIC GENERATORS, OR IN WHICH THE TONES ARE SYNTHESISED FROM A DATA STORE
    • G10H2240/00Data organisation or data communication aspects, specifically adapted for electrophonic musical tools or instruments
    • G10H2240/121Musical libraries, i.e. musical databases indexed by musical parameters, wavetables, indexing schemes using musical parameters, musical rule bases or knowledge bases, e.g. for automatic composing methods
    • G10H2240/131Library retrieval, i.e. searching a database or selecting a specific musical piece, segment, pattern, rule or parameter set
    • G10H2240/141Library retrieval matching, i.e. any of the steps of matching an inputted segment or phrase with musical database contents, e.g. query by humming, singing or playing; the steps may include, e.g. musical analysis of the input, musical feature extraction, query formulation, or details of the retrieval process
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10HELECTROPHONIC MUSICAL INSTRUMENTS; INSTRUMENTS IN WHICH THE TONES ARE GENERATED BY ELECTROMECHANICAL MEANS OR ELECTRONIC GENERATORS, OR IN WHICH THE TONES ARE SYNTHESISED FROM A DATA STORE
    • G10H2250/00Aspects of algorithms or signal processing methods without intrinsic musical character, yet specifically adapted for or used in electrophonic musical processing
    • G10H2250/131Mathematical functions for musical analysis, processing, synthesis or composition
    • G10H2250/215Transforms, i.e. mathematical transforms into domains appropriate for musical signal processing, coding or compression
    • G10H2250/235Fourier transform; Discrete Fourier Transform [DFT]; Fast Fourier Transform [FFT]
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/08Speech classification or search
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/20Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech

Description

200307205 Ο) 玖、發明說明 【發明所屬之技術領域】 本發明通常是有關於在一大音頻檔案資料庫上處理音 頻訊號。更尤其是,它是有關於一種用以快速並準確地決 定兩個音頻試樣是否匹配,以及是否免於含播放速度變動 之各種變換之發明技術。本發明技術更能準確估算變換。 【先前技術】 用以快速且準確地自動辨識音樂及其它音頻訊號之需 求持續成長。先前可用之音頻辨識技術常因準確性、或免 於噪而犧牲速度。在某些申請案中,要在極度噪訊存在 時估算時間對時間之分佈圖斜率時需要計算迴歸分析,這 導引出許多難度及低效能之速度和準確性。先前既存之音 頻辨識技術在重大播放速度變動存在中,因此無法實施快 速及準確之辨識,例如,無法辨識以高於正常速度播放時 之錄音。 增加問題複雜性的是DJ在無線電台、倶樂部和其它 場所所使用之漸增受到歡迎種類之速度變動,修正# $ 節奏變動。目前’不管播放速度變動及/或修正音調節奏 變動的話,無能實施快速且準確音頻辨識之強力且可靠之 技術。 【發明內容】 本發明提供一種說明兩音頻檔案之間關係特徵之快速 -6 - (2) (2)200307205 且不變動之方法,完成音頻辨識技術之要求。本發明方法 克服既有技術之以上所提及缺點,甚至在極度噪訊存在時 亦爲準確。 根據本發明一觀點,兩音頻試樣間之關係具首先匹配 得自各別試樣之某種指紋物件之特徵。對各音頻試樣產生 一組指紋物件。各指紋物件是發生在各別音頻試樣內之一 特別位置。各位置之決定依各別音頻試樣內容而定且各指 紋物件在或接近各特別位置處具備各別音頻試樣之一或更 多局部特性。在一實施例中,各指紋物件具備一變動成份 和不變動成份之特徵。接著決定各對匹配指紋物件之一相 對値。然後產生相對値之柱狀圖。如在柱狀圖中發現統計 上之明顯尖峰,則兩音頻試樣具備,例如,實質上匹配之 特徵。 根據本發明另一^觀點’藉由以柱狀圖一軸上之一尖峯 位置提供一全面相對値之估算可進而加強上述技術。接著 ,藉選取關注尖峯鄰近區並在所選取鄰近區計算一相對平 均値可改進全面相對値。 還有在從柱狀圖尖峯決定一相對播放速度値之另一實 施例中,對各對匹配之指紋物件計算一補償之相對時間偏 置値。根據補償之相對時間偏置値產生另一柱狀圖。如在 第二柱狀圖中發現統計上之明顯尖峯時,兩音頻試樣間之 關係則進而具備尖峯特徵,進而加強本發明之準確性。 【實施方式】 -7- (3) (3)200307205 本發明能在一大音頻檔案資料庫上作快速、強力、不 變動、及比例化之編列索引及搜尋,並對音頻圖樣辨識應 用特別有用。在某些實施例中,此處所發表之技術改進並 增強在以上所參考之美國專利申請案中所發表之音頻辨識 系統和方法。 兩音頻試樣檔案間之非常快速與有效比較運算在建立 一商業上可存活之音頻辨識系統中是必要的。根據本發明 一觀點’兩音頻試樣間關係具備得自如第i圖中所示,各 別音頻試樣頻譜之第一匹配某種指紋物件之特徵。頻譜爲 一時間、頻率代表/分析,它是以滑動窗框中一次取樣 2*K並計算傅立葉(Fourier)變換產生的,因此在各音框 中產生K頻箱。音框可重疊加以改進時間之解析分析。 使用之特別參數依處理之音頻試樣種類而定。最好使用取 樣率8KHz,K = 5 12之音框,和跨步爲64試樣之分散時間 音頻檔案。 指紋物件 產生各音頻試樣之音譜後,如第2圖中所示,篩檢求 得局部特性,例如局部能量尖峯。匹配程序藉由從各音頻 試樣之對應局部特性抽取一組指紋物件而開始。在一典範 實施例中,一音頻試樣爲一要加以辨識之未知聲音試樣而 另一音頻試樣爲一儲存在資料庫中之已知錄音。各指紋物 件發生在各別音頻試樣內之一特別位置。在某些實施例中 ’各指紋物件位在一音頻檔案內之某些時間偏置位置並在 -8 - (4) (4)200307205 接近其各別時間座標位置,包含有關音頻檔案之一組敘述 資訊。那就是,依接近各別時間偏置之音頻試樣而定加以 計算各指紋物件中所包含之敘述資訊。這被編碼成一小資 料結構。最好,以通常可再生,甚至存在噪訊,失真,及 如變動播放速度之其它變換之方式,決定位置和敘述資訊 。在這情況中,依各別音頻試樣之內容而定,決定各位置 ’且各指紋物件具備在或接近如第1圖中所示,例如,位 置(tl,fl )或(t2,f2 )之各別特別位置處各指紋物件 具各別音頻試樣之一或更多局部特性。 在一典範實施例中,各指紋物件具備其位置,變動成 份、和不變動成份之特徵。各局部特性爲一音譜尖峯並從 一對應音譜尖峯之頻率座標決定各頻率値。尖峯之決定是 藉在各時間-頻率座標附近加以搜尋並選取比其鄰近具較 大値之點。更明確地說,如第2圖中所示,將一音頻試樣 210分析成在區域221和222表示高能量之頻譜代表220 。抽取與局部能量區22 1和222有關之資訊並將其摘要成 一指紋物件 231,23 2等之淸單 230。各指紋物件選擇性 地包含一位置欄242,一變動成份252,及一不變動成份 262。最好,選取鄰近區,使得各選取點在以其爲中心之 一 21x21單位區內爲最大。讀者可參考以上所參考之美國 專利申請案,更加討論鄰近區及點之選取。接著’對各對 匹配之指紋物件,決定一相對値。在某些實施例中’相對 値爲各別音頻試樣參數値演算之商或差。然後產生一相對 値之柱狀圖。如果在柱狀圖中發現一統計上之明顯尖峯’ -9 - (5) 200307205 則兩音頻試樣具實質上匹配之特性。 參考第3圖,分別如音頻試樣1和2之以上說明 別備製指紋物件淸單3 1 0和3 20。從各淸單比較各指 件 3 1 1和3 22。在步驟351中,例如使用各不變動 1NV和1NV'將匹配指紋物件配成對,並在步驟352 其放在一淸單中。在步驟3 5 3中,計算各匹配對之相 。接著,在步驟3 5 4中,產生一相對値之柱狀圖。在 3 5 5中,在柱狀圖中搜尋一統計上之明顯尖峯。在 3 5 6中’如找不到,則音頻試樣1和2不匹配,例如 4 A圖之柱狀圖4 1 0。另外,如偵測到一統計上之明顯 ,則音頻試樣1和2匹配,例如爲第4 B圖之柱狀圖 〇 如第3 6 1步驟中之說明,藉由以柱狀圖一軸上 尖峯位置提供一全面相對値R之估算可進而加強上述 。在某些實施例中,首先選取所關注尖峯鄰近區能將 調。在第1圖中,這以一特殊位置(11,f 1 )附近之 注區1 1 0表示。接著,計算所選取鄰近區中之平均相 。這平均値可爲在所選取鄰近區中以數點各相對値計 權之平均値。在某些實施例中,能進而將R細調,對 配配對產生相對時間偏置値f_R*t。以這些相對時間 値’步驟362-3 64表示產生一第二柱狀圖,允許計算 償時間偏置。 例如,可實施其它種之時間-頻率分析,加以抽 紋物件,例如爲W i g n e r - V i 11 e分佈或微波。而且,不 ,分 紋物 成份 中將 對値 步驟 步驟 爲第 尖峯 420 之一 技術 R細 一關 對値 算加 各匹 偏置 一補 耳又指 用頻 -10- (6) (6)200307205 譜圖尖峯,亦能使用例如爲倒頻譜係數之其它特性。而且 ,可使用超解析技術,得到由頻譜尖峯所提供之時間-頻 率座標之更細微頻率和時間估算。例如,可使用有關頻率 箱之拋物線內插法增加頻率解析度。在朱利亞斯(史密斯 三世(Julius 0. Smith III)和薩比亞 西拉(Xavier Serra )之’’PARSHL :根據正弦波代表,對非諧和聲音之 分析/合成程式’’,國際電腦音樂會議錄(ICMC-87,東京 ),電腦音樂協會,1 987,及Prentice Hall公司所出版 由史提芬凱(SterenM.kay) ( 1988年元月)所著之’·現 代頻譜估算:理論與應用’’中可發現相關之典範教義,此 處將後兩者納入參考。 匹配處理 在一匹配運算中,經由其各別指紋物件比較兩音頻試 樣。如以前參考第3圖之討論,產生匹配指紋物件配對, 各配對實質上包含匹配成份。備置資料,允許快速搜尋之 一種方式爲將指紋物件編碼成數値標識,如3 2位元無符 號之整數,並使用數値標識作爲儲存和搜尋之關鍵。例如 在艾迪生衛斯理(Addison Wesley)公司所出版,由唐納 歐文努斯(Donald Ervin Km uth) ( 1998年4月7所著之 ”計算機程式規劃技術,第3冊:儲存和搜尋(第2版)” 中熟知有效資料處理技術,此處將其納入參考。 在一典範實施例中,各指紋物件包含一不變動成份和 一變動成份。不變動成份指的是對應於頻譜尖峯之頻率値 • 11 - 200307205 比率,而且在時間延長下,頻譜尖峯間之時間差(即,時 間差距)比率不變動。例如,參考第5 A和5 B圖,如音 頻試樣頻譜在座標(tl,fl ) , ( t2,f2 ),和(t3,f3 )是某些局部頻譜尖峯,則對於兩點之不變動量爲f2/fl ,即f2’/fl,= f2/fl。額外3點之不變動量指定爲f3/fl,( t3-tl) /(t2-tl),或(t3-t2) /(t2-tl),或藉變更這些 點及/或計算這些數量或其組合之函數加以產生任何其它 組合。例如,f2/fl除以f3/fl可產生f2/f3。而且,如使 音頻試樣線性延長,如只是快速播放,則頻率和時間差額 外地享受交互關係,故如fl* ( t2-tl )之數量亦爲不變動 量。可使用這些數量之對數,以加減取代乘除。爲探求頻 率和時間延長之比,假設他們無相依性,故具有一頻率變 動量和一時間變動量是必要的。爲使匹配運算有效率,我 們使用不變動部位編列指紋索引並使用近似或正確値加以 搜尋。使用近似匹配加以搜尋允許某些特別強韌性,對抗 失真及圓弧化誤差,但如果搜尋不變動成份變成多維範圍 之搜尋則產生更多成本。在較佳實施例中,需要正確匹配 各指紋物件之不變動成份,因此產生一非常快速之系統, 爲了噪訊存在之辨識而對敏感度有一些妥協。重要的是要 注意甚至在對應之音頻試樣中,只有少數指紋物件正確匹 配,則這方法亦運作良好。在柱狀圖尖峯偵測步驟中,甚 至如果正確匹配並殘存少如1 -2%之指紋物件則在統計上 明顯有一尖峯。 除了,或不用不變動成份外,亦能使用變動成份,減 -12- (8) 200307205 小匹配指紋物件之個數。例如,我們可能需要來自 頻試樣之一變動成份 V在+/-20%內匹配來自第二 樣之一對應成份V。在那情況中,我們可形成一 識代表’使得上部位(例如,最高有效位元)包含 成份,而下部位(例如,最低有效位元)包含變動 然後’搜尋一近似匹配變成在使用變動成份之最低 値組成之標識上作範圍搜尋。如使用一變動成份完 ,則因此未必嚴格需要在匹配運算時使用不變動成 而,建議在匹配程序中使用不變動成份,因它有助 似匹配之個數,因此使柱狀圖編程程序有效率並降 一般開銷量。 另一方面,新變動成份本身可能是或不是兩指 間匹配準則之一部分。變動成份之代表値可因從一 音至一取樣錄音之某些參數變換而失真。例如,可 fl,f2,f3之頻率變動成份以及如(t2-tl) ,( t3 (t3-t2 )之時間變動成份作爲播放速度之變動成份 第二音頻試樣,例如,授引自資料庫之匹配試樣有 爲(tl’,fl,) , ( t2’,f2’)和(t3,f3’)之頻譜 座標對應於以第一音頻試樣所列之相同點。然後, 份Π’可能有一比例化之値fl’ = Rf*fl,其中,Rf爲 延長參數,說明多快或多慢會將第一試樣錄音與第 比較。可使用各兩匹配音頻試樣之變動成份藉雨 Rf = fl’/fl間之比率加以計算說明一巨觀參數之全 値之估算。這指定兩匹配時間-頻率點之相對音調 第一音 音頻試 數値標 不變動 成份。 和最局 成搜尋 份。然 降低疑 低處理 紋物件 原始錄 選取如 -tl )或 。假設 一座標 ,這些 頻率成 一線性 二錄音 頻率値 面延長 比;例 -13- 200307205 Ο) 如,Rf = 2意爲第一音頻試樣爲第二音頻試樣音調 )之半。另一可能性爲使用Rt= ( )/ ( t2-tl 這情況中,相對値R爲相對播放速度比,即,Rt = 第一音頻試樣播放速度爲第二音頻試樣之兩倍。 如 Rf=l/Rt,即,f,/f= ( t2-tl) / ( 12111), 這種音頻試樣之交互時間-頻率關係,兩音頻試樣 性時間延長關係。在這情況下,我們可使用此處所 柱狀圖編程法,形成估算利用對應變動頻率成份之 率比Rf,且再次形成估算相對播放速度Rt,然後 較加以偵測播放關係是否爲線性或非線性。 通常,利用來自第一和第二音頻試樣之對應變 ,從所匹配之指紋物件加以計算一相對値。相對値 率之簡單比或時間差,或造成估算用以說明第一與 頻試樣間映射之全面參數的某些其它函數。但通常 例如爲 R = F ( v 1,v Γ )之任何兩個輸入之函數F ( 其中,vl和vl’各爲變動量。最佳者爲F()爲 函數,使得測量v1和v Γ時之小誤差在輸出R形 差。 柱狀圖編程 如此處之說明,對從指紋物件之匹配配對淸單 之相對値組產生一柱狀圖。然後在柱狀圖中搜尋一 柱狀圖中’統計上存在之明顯尖峯表示已發生可能 。這種方法不用如(tl,-t’)之時間偏置差,而在 (頻率 )。在 2意爲 則由於 有一線 發表之 相對頻 實施比 動成份 可爲頻 第二音 可使用 ), 一連續 成小誤 所計算 尖峯。 之匹配 柱狀圖 -14- (10) 200307205 中特別搜尋相對値之集叢。 根據本發明之原理,柱狀圖之作用在形成計數値箱, 各箱相當於沿著柱狀圖獨立軸之一特定値。爲達本發明之 目的,柱狀圖之產生可就對相對値淸單之分類加以完成。 因此,偵測相對値淸單之柱狀圖尖峯之一種快速和有效方 式爲將淸單由小至大分類,然後篩檢找出具相同或類似値 之最大塊項目。 統計意義 如本發明此處之討論,甚至假如只有少如2%之指紋 物件倖免於所有失真並匹配無誤時,兩音頻試樣亦能匹配 無誤。藉由記下兩音頻試樣間之比較刻痕,這是可能的。 明確地說,在柱狀圖尖峯附近選取一鄰近區並計數落在鄰 近區中之所有匹配配對,記下刻痕。此外,可計算權重點 數,扣減離尖峯中心較遠之配對之貢獻。 估算截止準則之一種方式爲假設非匹配音軌刻痕之槪 率分佈以指數末尾往下掉。將這模式套用在實際所測量之 非匹配音軌刻痕分佈。接著,對於一 N音軌資料庫,計 算最局刻痕之累積槪率分佈(例如,取一單一非匹配刻痕 之累積槪率分佈之第N階指數)。一旦知道槪率曲線並 選取僞正量之一最大位準時(例如,0.5% ),則可選取一 數字臨界値並用以決定柱狀圖尖峯之匹配配對是否有一統 計上明顯之個數。 -15- (11) (11)200307205 超精細估算 一旦找到一統計上明顯之柱狀圖尖峯,則可計算全面 相對値(如相對播放速度)之高解析”超精細”估算。這種 計算之完成是藉由在尖峯附近選取一鄰近區,例如,包含 離尖峯柱狀圖箱,中心約3或5箱寬之間隔,並計算鄰近 區中之平均相對値。使用這種技術,我們可發現準確性達 0.05%內之相對播放速度。以此處所發表之偏置引申,可 以優於1 m s之準確性估算全面之時間偏置,該準確性比以 上所討論之頻譜音框之時間解析更精細。 強力迴歸分析 如以上所參考之美國專利申請案中之討論,在試樣真 正匹配之情況中,如第6A圖中所示,在匹配試樣之匹配 指紋物件之對應時間座標(t ’,t )彼此相對所劃之分佈圖 中可看到一斜線。難題是在高噪訊量存在中找尋迴歸方程 式,它是由斜線之斜率和偏置所決定的。斜率表示相對播 放速度,而偏置爲一音頻試樣一開始對第二音頻試樣一開 始之相對偏置。習知上有如最低均方調和之迴歸技術,例 如,爲威廉培斯(w i 11 i a m Η ο P r e s s ),布萊恩佛萊納利 (Brian P. Flannery ),沙烏提可夫斯基(S a u 1 A · Tenkolsky),及威廉維特寧(William T. VeHerling)( 1 9 9 3年元月)在劍橋大學校刊所著之”以C寫成之數値秘 笈:科學計算之技術(第二版)”,此處將該文納入參考 。不幸地,習知技術苦於不相稱之敏感度,其中,單一之 -16- (12) 200307205 遠局外物可使所估算之迴歸參數急劇傾斜。實際上,相對 點常由局外物主導,使其非常難以偵測正確斜線。強力迴 歸分析之其它技術可用以克服局外問題,在噪訊存在之相 對點之間找到線性關係,但這些傾向於緩慢與反覆且在局 部最佳化中可能卡住。在找尋一未知線性迴歸變數之文獻 中存在廣大各種技術。從數學作品(Math works )及此處 所納入參考之M a 11 a b工具幫手包含迴歸分析用之各種軟 體常規。 本發明提供估算相對播放速度(或,在線性播放關係 情況下,對等地爲相對音調之例數)之發明方法,該方法 解決問題,甚至假如匹配之斜率不等於1時,如第6B圖 ,在時間-時間分佈圖中找到一迴歸線。如此處之發表, 使用局部相對播放速度之柱狀圖,利用先前未考慮之資訊 並提供快速且有效解決迴歸分析問題之未預期優點。 爲找尋遍偏置,假設對應之時間點具下列關係。 偏置= tl’-Rt*tl,其中,Rt由先前之討論求得。這爲 補償之時間偏置且作用在使兩音頻試樣間之時間座標系統 正常化。這在如構成第7A圖中未知斜率之斜線及第7C 圖中垂直線之時間-時間分佈圖之剪力變換亦可看到。第 7B圖之柱狀圖720說明表示全面相對播放速度比R之累 積相對播放速度比尖峯。然後由偏置公式指定新相對値, 如第7D圖中所見到的,產生一新的柱狀圖740。新柱狀 圖740之尖峯指定全面偏置之估算値,如上述,利用尖峯 鄰近區之平均値,該估算値可會是陡峭的。 -17- (13) 200307205 槪要言之,第一柱狀圖編程階段提供一種方式加以估 算相對播放速度,以及決定是否存在匹配。第二柱狀圖編 程階段確信候選匹配音頻試樣有明顯個數之亦暫時對齊之 指紋物件。第二柱狀圖編程階段亦作爲一第二獨立篩檢準 則並有助降低僞正量之槪率,因此提供較有力準則加以決 定兩音頻試樣是否匹配。只在第一柱狀圖中如有一統計上 之明顯尖峯時可選擇實施第二柱狀圖之編程階段,因此節 省計算資源和努力。可選擇實施進一步之最佳化,例如, 降低計算上之混亂’不用對淸單上匹配指紋物件之所有配 對計算第二柱狀圖’第二柱狀圖可只使用對應於第一柱狀 圖尖峯之匹配對加以產生。 多重錄音之同步處理 本發明之執行可用以對非同步之音頻錄音加入旁白及 時間校準。例如,假設在稍微不同位置或環境,以不同麥 克風獨立操作一 DAT錄音機和一卡帶錄音機。如稍後預 期要從各別錄音機將兩段錄音組合成一段混音,則可使用 此處說明之強力迴歸分析技術兩音軌同步化,得到時間偏 置。照這樣’甚至假如非同步化之錄音機以稍微不同速度 操作時可以局度準確性決定相對速度,允許參考另一段錄 音補償一段錄音。如發現其中一段錄音已損毀且需從另一 音源加以補遺時,這尤其有用。如此處說明之時間校準和 同步化因此允許透通性混音。 -18- (14) (14)200307205 資料庫搜尋 因比較方法極快速,可能要將一大資料庫之變頻試樣 預先處理成各別之指紋物件淸單。因一嫻熟此技術者會認 知到,使用目前可用之資料處理技術因此可將一未知音頻 試樣預先處理成指紋物件之其本身各別淸單。使用資料庫 中預先處理之指紋物件,然後可實施上述之匹配,柱狀圖 編程,及尖峯偵測技術加以找尋匹配。 雖然已詳細說明本發明及其優點,應了解的是本發明 並不限於或被界定成此處所表示者或所討論者。尤其是, 此處所發表之圖示和說明以圖例解釋有關本發明之技術, 表示本發明之實例,並提供利用本發明之實例且不可推斷 爲使本發明受到限制。已知之方法,技術,或系統可不詳 細加以討論,故能避免模糊本發明之原理。因以技術中之 其中一項平常技能將會認知到,只要不偏離本發明之原理 和精神,對本發明可加以實施,修飾,或另外改變。例如 ’可以在電腦可讀取媒體中具體之電腦可執行指令之形式 加以實施或另外實現此處所說明之方法,技術,和步驟。 另外’本發明可在一具有客戶終端和伺服器之電腦系統中 加以實施。客戶終端傳送第一和第二音頻試樣間關係特徵 所需之’例如,爲指紋物件之資訊至表現特徵之伺服器處 。因此’應以下列請求項目及其法律上之等效請求項加以 決定發明範圍。 【圖式簡單說明】 -19- (15) (15)200307205 第1圖代表一分析音頻試樣之頻譜圖。 第2圖爲一表示根據本發明一觀點,產生自一音頻試 樣指紋物件之範例圖。 第3圖說明根據本發明原理所比較之兩音頻試樣。 第4A-B圖表示具有及不具一統計上明顯尖峯之典範 柱狀圖。 第5 A-B圖說明當播放速度變動時,時間-頻率點之運 動。 第6A-B圖表示匹配混合標識之第一音頻試樣(試樣 聲音)和第二音頻試樣(資料庫聲音)之對應時間。當試 樣聲音之播放速度與資料庫聲音相同時,斜率爲i。 第7A-D圖說明找到並繪製本發明柱狀圖技術之快速 及有效斜率。 元件對照表 2 1 0 :音頻試樣 220 :頻譜 221,222 :能量區 230 :淸單 231:,232 :指紋物件 242 :位置欄位 252 :變動成份 262 :不變動成份 3 10,320 :指紋物件淸單 200307205 (16) 1,2 :音頻試樣 3 1 1,3 2 2 :指紋物件

Claims (1)

  1. 200307205 Π) 拾、申請專利範圍 1 · 一種具備一第一和一第二音頻試樣之間關係特徵 之方法,包含以下步驟: 產生第一音頻試樣之第一組指紋物件,各指紋物件發 生在第一音頻試樣內之一各別位置,各別位置之決定依第 一音頻試樣內容而定,且各指紋物件在或接近各別位置處 具備第一音頻試樣之一或更多特性; 產生第二音頻試樣之第二指紋物件,各指紋物件發生 在第二音頻試樣內之一各別位置,各別位置之決定依第二 音頻試樣內容而定,且各指紋物件在或接近各別位置處具 備第二音頻試樣之一或更多特性; 藉由使來自第一音頻試樣之第一指紋物件和來自第二 音頻試樣,實質上類似於第一指紋物件之第二指紋物件相 匹配而使指紋物件配成對; 根據配對步驟,產生所匹配指紋物件之配對淸單; 決定各對匹配指紋物件之相對値; 產生一相對値之柱狀圖;以及 在柱狀圖中搜尋一統計上之明顯尖峯,該尖峯具備第 一和第二音頻試樣間關係之特徵。 2. 如申請專利範圍第1項之方法,其中,如發現一 統計上之明顯尖峯時,則第一和第二音頻試樣間之關係具 備實質上匹配之特徵。 3. 如申請專利範圍第1或2項之方法,更包含以柱 狀圖軸上一尖峯位置加以估算一全面相對値之步驟’全面 -22- (2) (2)200307205 相對値更具備第一和第二音頻試樣間關係之特徵。 4. 如申請專利範圍第3項之方法,更包含超精細估 算全面相對値之決定步驟,其中之決定步驟包含: 在尖峯附近選取一鄰近區,以及 在鄰近區中計算一平均相對値。 5. 如申請專利範圍第1項之方法,其中,各指紋物 件具一不變動成份,而各對匹配之指紋物件中之第一和第 二指紋物件具匹配之不變動成份。 6. 如申請專利範圍第5項之方法,其中,使用至少 以下之一,產生不變動成份: (i ) 一第一和一第二頻率値間之比率,從接近各指 紋物件各別位置之第一和第二局部特性分別決定各頻率値 (ii ) 一頻率値和一時間差値間之乘積,從第一局部 特性決定頻率値,並在接近各指紋物件各別位置之第一局 部特性與第二局部特性之間決定時間差値;以及 (iii ) 一第一和一第二時間差値間之比率,從第一和 第二局部特性決定第一時間差値,從第一和第三局部特性 決定第二時間差値,各局部特性接近各指紋物件之各別位 置。 7 ·如申請專利範圍第6項之方法,其中,各局部特 性爲一頻譜尖峯並從一對應頻譜尖峯之一頻率座標決定各 頻率値。 8·如申請專利範圍第1或5項之方法,其中,各指 -23- (3) (3)200307205 紋物件具一變動成份,並利用第一與第二指紋物件之各別 變動成份加以決定各對匹配指紋物件之相對値。 9.如申請專利範圍第8項之方法,其中,變動成份 爲從接近各指紋物件之各別位置之一局部特性所決定之頻 率値,使得一對匹配指紋物件之相對値具備第一和第二指 紋物件各別頻率値比率之特徵,且柱狀圖中之尖峯具備第 一和第二音頻試樣間關係之特徵而第一和第二音頻試樣具 備相對音調之特徵,或在線性延長之情況中爲相對播放速 度之特徵。 10·如申請專利範圍第9項之方法,其中,各別頻率 値之比率具備成除法或差法演算之特徵。 11·如申請專利範圍第9項之方法,其中,各局部特 性爲一頻譜尖峯並從一對應頻譜尖峯之一頻率座標決定各 頻率値。 1 2 ·如申請專利範圍第8項之方法,其中,變動成份 爲從接近各指紋物件之各別位置之第一和第二局部特性所 決定之時間差値,使得一對匹配指紋物件之相對値具備各 別變動時間差値比率之特徵,且柱狀圖中之尖峯具備第一 和第二音頻試樣間關係之特徵而第一和第二音頻試樣具備 相對播放速度之特徵,或在線性延長之情況中爲相對音調 之特徵。 1 3 .如申請專利範圍第1 2項之方法,其中,各別變 動差値之比率具備成除法或減法演算之特徵。 1 4 .如申請專利範圍第1 2項之方法,其中,各局部 -24- (4) 200307205 特性爲一頻譜尖峯並從一對應頻譜尖峯之一頻率座 各頻率値。 1 5 ·如申請專利範圍第8項之方法,更包含以 利用各別變動成份決定第一和第二音頻試樣之 音調,其中,各變動成份爲從接近各指紋物件各別 一局部特性所決定之頻率値; 利用各別變動成份決定第一和第二音頻試樣之 速度,其中,各變動成份爲從接近各指紋物件各別 一第二局部特性所決定之時間差値;以及 偵測相對播放速度之相對音調和倒數實質上是 ,在這情況下,第一和第二音頻試樣間之關係具備 特徵。 16.如申請專利範圍第1項之方法,其中,R 相對値條狀圖之尖峯所決定之相對播放速度値,更 下步驟: 對於淸單中之各對匹配指紋物件,決定一所補 時間偏置値,,其中,t和Γ爲有關第一和第 物件之時間位置; 產生所補償相對時間偏置値之第二柱狀圖;以 在所補償相對時間偏置値之第二柱狀圖中搜尋 上之明顯尖峯,該尖峯更具備第一和第二音頻試樣 之特徵。 1 7 · —種用以實施如申請專利範圍中任一先前 標決定 下步驟 一相對 位置之 一相對 位置之 否相異 線性之 爲一從 包含以 償相對 二指紋 及 —統計 間關係 項之方 -25- (5) (5)200307205 法之電腦程式產品。 18. 一種用以實施如申請專利範圍第1至1 6項中任 一項方法之電腦系統,該電腦系統包含一客戶終端用以將 第一與第二音頻試樣間關係特徵所需之資訊傳送至一表現 該特徵之伺服器。
    -26-
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