在哼唱式音樂搜尋系統中，對人聲哼唱的起音點偵測效能的好壞，左右著音符切割準確率，進而影響著哼唱搜尋的效能。哼唱信號的起音點偵測一直都是困難的問題，目前的偵測效能仍然不高。本論文的目的，即是希望能改進目前在哼唱信號的起音點偵測效能，再利用改進後的起音點偵測方法來增進音符切割的準確性，進一步地提升哼唱式搜尋的效能。在起音點的偵測上，我們提出移動平均濾波的概念，並且應用於現有的起音點偵測函數中，其實驗結果，能有效的改進原有起音點偵測函數的效能。在起音點的決策上，我們改進原有單一決策特徵來進行決策的方法，使用多個決策特徵值，透過高斯混合模型進行訓練起音點模型，並利用此模型來制定起音點的決策。在實驗的結果能有效地提升哼唱人聲哼唱的起音點偵測效能。在公開的人聲哼唱資料庫(QBSH) 上測試可以達到77.4%的F度量(F-measure)值、相當於76.9%的召回率(recall)與77.7%的精確率(precision)。
最後利用此起音點偵測架構套用於哼唱式搜尋系統中，結合我們的旋律比對系統。實驗結果能將原有哼唱式搜尋系統效能從MRR值0.53提升至0.56，前15名入選率從67%提升至70%，表示改進起音點偵測方法能有效地提升哼唱式搜尋的效能。

Onset detection for singing voices is an important but difficult problem for note detection in query by singing/humming or music transcription. The purpose of this paper is to improve the performance of onset detection for singing/humming voice. This paper proposes an onset detection scheme which utilizes the moving average filtering in detection function to accentuate the uprising margins, while making use of discriminative classifier based on Gaussian mixture models to combine relevant features of adjacent peaks in final decision. Experimental results show that the onset detection scheme can improve the detection performance significantly, and achieve 77.7% of precision rate and 76.9% of recall rate at 77.4% of F-measure.
This onset detection scheme was further combined with the query by singing/humming system, and experimental results show that, the onset detection to detect note can effectively improve the performance of music search. The MRR value can be increased from 0.53 to 0.56 and increase the top-15 hit rate from 67% to 70% when onset detection is applied to the note detection.

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