隨著多媒體擷取技術之進步，多媒體資料已和我們的生活更加緊密結合，尤以音樂為盛。如何準確且有效率地擷取使用者感興趣的音樂是音樂搜尋之主要目的。由於音樂資料是屬於時序性資料，目前許多研究除了利用音樂的低階特徵值外，也同時考慮音樂之時序意義，試著找出關聯性以相互結合。然而，許多方法不斷致力於追求更高準確度之同時，卻往往忽略了效率的考量。在本研究中，我們發展三種快速樣式索引技術，利用編碼原則，讓音樂片段以低維度的群組定義來取代高維度之低階特徵值，藉以降低計算複雜度，同時考慮存在於音樂中的連續關聯性，搭配其關聯程度來產生關聯樣式組合，以提昇搜尋準確率，並透過獨特設計之快速樣式索引樹，可快速地找到在資料庫中最有可能包含該片段的音樂。實驗結果顯示，我們所提出之方法可以在很短的執行時間內完成音樂搜尋，在準確度上也有很好的表現。

With the progress of multimedia capturing techniques, multimedia data, especially music data is more and more tightly bound up with our life. How to access the user-interested music efficiently and effectively is a hot topic for content-based music retrieval. Because music data is temporal, many previous works used not only the low-level features of music itself but also the sequence of the features. Most of the works put their concentration on enhancing the accuracy but ignore another important issue: the efficiency. In our method, the high dimensionality of low-level features is first reduced to improve the retrieval performance by the labeling approach. Then we make use of the temporal continuity to find the valuable patterns within a sliding window and construct the index structure. Through our proposed method, the music that is related to user’s query can be found rapidly.

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