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研究生: 陳進旺
CHIN-WANG CHEN
論文名稱: 應用AMDF演算法之音高點擷取方法
A Pitch Extration Method Based on the AMDF Algorithm
指導教授: 楊傳凱
Chuan-Kai Yang
口試委員: 羅乃維
Nai-Wei Lo
林伯慎
Bor-Shen Lin
學位類別: 碩士
Master
系所名稱: 管理學院 - 資訊管理系
Department of Information Management
論文出版年: 2007
畢業學年度: 95
語文別: 中文
論文頁數: 45
中文關鍵詞: 音高偵測音調基頻
外文關鍵詞: AMDF, pitch detect, local minima, fundamental frequency
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  • AMDF音高偵測演算法最常被詬病的是常常會發生判斷錯誤之問題,因此各種不同的改良式演算法紛紛地被提出來,但大部分都是著眼於演算法之運算公式改良的議題,而其實在演算法運算以後,Local Minima音高點之取點方式也是一個決定所偵測音高準確性的重要步驟。

    本論文提出一個在AMDF演算法後之音高點擷取方法,主要分為三個步驟:第一個步驟是將每個位移點經AMDF演算法所求出的值,取出所有的Local Minima點,並經一個Clipper機制將大於限定值的Local Minima點去掉;第二個步驟是將剩下的Local Minima點進行相鄰點之比對篩選,只保留AMDF值最小的Local Minima點;最後一個步驟就是由所剩下來的Local Minima音高點中找出它的基本週期,且在過程中所找出之基本週期誤差率要低於音階誤差率之限制。

    利用此方法來作音高點的擷取,可以有效解決並改善傳統AMDF演算法的缺失,單純以Local Minima最小值來取點所造成之誤判情況,雖然在取點運算時會增加一些運算時間,但整體來看,在兼顧準確度與運算速度上仍然可以接受。


    Average Magnitude Difference Function (AMDF) is a pitch detection algorithm that is known to often suffer from the problem of pitch misjudgments. Various algorithms have thus been proposed to address this problem. However, as most of these algorithms focus on how to improve the procedure of AMDF itself, we argue that extracting the Local Minima pitch points may also play a critical role on enhancing the pitch detection accuracy, after applying the original AMDF algorithm.

    This paper proposed a pitch point extraction algorithm which improves over the traditional AMDF algorithm, and it consists of three steps. First, we apply the AMDF algorithm to calculate the value of each transformed point, locate all Local Minima points, and discard those points whose values are greater than a threshold. Second, we compare the remaining Local Minima points with their neighboring points and retain only the smallest values around the Local Minima points. Finally, we determine the Local Minima fundamental frequency, under the constraint that the fundamental frequency discrepancy rate must be lower than a pre-specified threshold determined from the chromatic scale discrepancy rate.

    Compared with the original version of AMDF, which may erroneously determine the pitch from the smallest Local Minima point, this proposed method could offer more accurate results. In spite of the additional overheads due to the processing of Local Minima points, the resulting accuracy is improved, while the overall calculation time is still well acceptable.

    第1章 緒論 1.1 研究背景與動機 1.2 研究方向 1.3 前人研究 1.4 章節概要 第2章 音訊處理背景知識介紹 2.1 甚麼是音高偵測 2.2 音高偵測演算法的種類 2.3 音框 2.4 頻域上(Frequency Domain)之音高偵測演算法 2.5 時域上(Time Domain)之音高偵測演算法 2.5.1 自相關函數(ACF)演算法 2.5.2 AMDF演算法 2.5.3 基頻的計算方式 2.6 實務上音高偵測之運作方式 第3章 AMDF音高點擷取方法 3.1 前言 3.2 AMDF音高偵測流程 3.3 使用AMDF演算法求出音高點 3.4 改良之Local Minima音高點擷取方法 3.4.1 Local Minima取點 3.4.2 音高點比對篩選 3.4.3 找出基本週期 3.5 計算基頻音高 第4章 成果分析 4.1 傳統常見誤判案例分析 4.2 本研究之方法誤判案例分析 第5章 結論與未來展望 5.1 結論 5.2 未來展望 參考文獻 附錄一 音階表

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