在本論文中，我們首先討論從多音資料庫中取回資訊時所常用的技術，並提出一個新的近似搜尋的方法。從一個巨大的音樂資料庫中查詢多音音樂是一個很有趣的問題，近來，很多的研究者嘗試提供能在多音資料庫有效率地依據內涵來查詢音樂的方法。但是大部份在近似搜尋時均無法順利進行。在本論文中，我們提出了三種多音音樂的表示法及對應的近似量測法，此外還提出一個能取回k首包含與查詢最近似之片段的樂曲。由於我們的近似量測是根據修訂距離，我們提出的方法先在查詢的音樂使用刪除及替換兩種運算來產生部份可能的結果之樣式，再借由串列形態之索引結構找出解答並提供額外的資訊以求得修訂距離，最後經由比較已知查詢與可能結果之修訂距離來加速查詢處理的速度。
隨著網路及多媒體的發展，越來越多的音樂可以在網際網路找到，因良好的分類可增加查詢結果的正確率，使得自動音樂分類成為重要的研究課題。在本論文中，我們提出依據內涵來自動分類音樂。在此方法中，我們使用樂曲中的重複樣型用來代表音樂的特色。我們首先量測各個重複樣型在各類別音樂中的重要程度，並計算需分類樂曲之重複樣型與已分類之重複樣型之距離及重要程度，最後依據各類別之分數高低決定該樂曲所屬類別。
從音樂擷取樣型是一個很有趣的問題，擷取出的樣型可用來作為音樂查詢或音樂分析的特徵。以往的研究大都專注於重覆樣型的擷取，但實際上音樂中重複的片段是會有變化的。在此論文的第三部份，我們提出一個新的方法能快速的找出所有的近似重覆樣型。在此方法中，先產生所有的音樂片段並對應至向量空間的位置，經由索引結構及範圍搜尋法預判可能的重複數量以決定是否可能為答案。
最後經由實驗的驗證，在此論文中所提出的三個方法在多音音樂查詢、音樂分類及樣型擷取均優於傳統的方法。

In this thesis, we first discuss the techniques used in polyphonic music information retrieval and propose a novel technique of similarity search. Querying polyphonic music from a large data collection is an interesting topic. Recently, researchers attempt to provide efficient methods for content-based retrieval in polyphonic music databases where queries are polyphonic. However, most of them do not work well for similarity search, which is important to many applications. In this thesis, we propose three polyphonic representations with the associated similarity measures and a novel method to retrieve k music works that contain segments most similar to the query. In general, most of the index-based methods for similarity search generate all the possible answers to the query and then perform exact matching on the index for each possible answer. Based on the edit distance, our method generates only a few possible answers by performing the deletion or replacement operations on the query. Each possible answer is then used to perform exact matching on a list-based index, which allows the insertion operation to be performed. For each possible answer, its edit distance to the query is regarded as a lower bound of the edit distances between the matched results and the query. Based on the kNN results that match a possible answer, the possible answers that cannot provide better results are skipped. By using this mechanism, we design a method for efficient kNN search in polyphonic music databases.
With the popularity of multimedia applications, a large amount of music data has been accumulated on the Internet. Automatic classification of music data becomes a critical technique for providing an efficient and effective retrieval of music data. In this thesis, we propose a new approach for classifying music data based on their contents. In this approach, we focus on music features represented as rhythmic and melodic sequences. Moreover, we use repeating patterns of music data to do music classification. For each pattern discovered from a group of music data, we employ a series of measurements to estimate its usefulness for classifying this group of music data. According to the patterns contained in a music piece, we determine which class it should be assigned to.
Pattern extraction from music strings is an important problem. The patterns extracted from music strings can be used as features for music retrieval or analysis. Previous works on music pattern extraction only focus on exact repeating patterns. However, music segments with minor differences may sound similar. The concept of the prototypical melody has therefore been proposed to represent these similar music segments. In musicology, the number of music segments that are similar to a prototypical melody implies the importance degree of the prototypical melody to the music work. In this thesis, a novel approach is developed to extract all the prototypical melodies in a music work. Our approach considers each music segment as a candidate for the prototypical melody and uses the edit distance to determine the set of music segments that are similar to this candidate. A lower bounding mechanism, which estimates the number of similar music segments for each candidate and prunes the impossible candidates is designed to speed up the process.

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