本論文提出了一種改進的哼唱檢索系統，能同時使用旋律和歌詞的資訊，以實現更好的性能。首先會進行哼唱分辨，以將「唱」和「哼」分離開來。對於「哼」的查詢，我們套用了只含音高的旋律辨識方法，其在參加MIREX的哼唱檢索項目時被使用，並在該比賽中排名第一。對於「唱」的查詢，我們將旋律辨識和歌詞辨識的分數結合，以利用額外的歌詞資訊。歌詞辨識是基於一個改良過的樹狀網路，此技術常用在語音辨識上。系統整體效能，以錯誤減少率來看，在兩個不同的實驗參數下的前20名之結果中，分別達到 39.01％和23.53％，說明了此系統的可行性。

This paper proposes an improved query by singing/humming (QBSH) system using both melody and lyrics information for achieving better performance. Singing/humming discrimination (SHD) is first performed to distinguish singing from humming queries. For a humming query, we apply a pitch-only melody recognition method that has been used for QBSH task at MIREX with rank-1 per-formance. For a singing query, we combine the scores from melody recognition and lyrics recognition to take advantage of the extra lyrics information. Lyrics recognition is based on a modified tree lexicon that is commonly used in speech recognition. The performance of the overall QBSH system achieves 39.01% and 23.53% error reduction rates, respectively, for top-20 recognition under two experimental settings, indicating the feasibility of the proposed method.

Chapter 1. Introduction 1
Chapter 2. System Overview 3
2.1 Phone and Syllable Similarity 4
2.2 Singing/Humming Discrimination 7
2.3 Lyrics Recognition 8
2.4 Lyrics Scoring and Combination 11
Chapter 3. Experiments 14
3.1 The Dataset 14
3.2 Experimental Results 14
3.2.1 Results of SHD 14
3.2.2 Lyrics recognition and Combined Results 18
Chapter 4. Conclusions and Future Work 25
References 26
Appendix A: The List of 156 Bi-phones 28
Appendix B: The List of 423 Syllables 29

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