臺灣博碩士論文加值系統

本論文的目標在於分辨輸入的聲音片段屬於歌唱聲(含有歌聲)或是樂器聲(不含歌聲)，研究焦點放於組合不同種類之特徵係數以找出最具有識別效果之特徵向量，在此採用的特徵係數包括梅爾倒頻譜係數、基週偵測特徵係數與Chroma延伸特徵，並且加入前列係數的差分值。所採用的辨識方法則是基於高斯混合模型(GMM)之方法，我們分別訓練8、16、32和64等不同混合數之高斯混合模型，再據以進行外部聲音的識別實驗。在音框為單位之識別實驗中，我們嘗試了6種組合的特徵向量，其中MFCC加上基週偵測係數可比MFCC顯著提升識別率，若再加上差分值及投票機制處理，則音框之識別率最高可達71.3%。在片段為單位之識別實驗，我們嘗試了8種組合的特徵向量，對於純樂器聲片段之識別，識別率最高的是40維係數組合之特徵向量，達到97.1%，對於混合聲片段之識別，識別率最高的是17維係數組合之特徵向量(MFCC+P)，達到94.7%，若以平均識別率來看，則最高的是40維係數之特徵向量，達到了93.8%。整體來說，組合MFCC、基週偵測係數、Chroma延伸特徵及它們的差分值之40維特徵向量，可得到最高的聲音片段識別率。

This thesis aims to recognize the class that an input sound clip belongs to. The two sound classes concerned here are singing sound (with vocal singing) and instrument sound (without vocal singing). The focus of this research is placed on testing different combinations of those considered acoustic features in order to find a most effective feature vector for sound class recognition. The acoustic coefficients considered here include mel-frequency cepstral coefficients (MFCC), pitch-detection coefficients (PDC), Chroma extended features, and their delta coefficients. The recognition method studied is based on Gaussian mixture model (GMM). Different numbers of mixtures, e.g. 8, 16, 32 and 64, are used to train the parameters of the GMMs. Then, these GMMs are used in the experiments for recognizing external sound clips. In the experiments for sound frame recognition, we have tried 6 different feature vectors, i.e. 6 different combinations of acoustic features. Among the 6 feature vectors, the vector, MFCC plus PDC, is found to be significantly better than MFCC only in recognition rate. If the feature vector is augmented with delta values and the processing of voting mechanism is added, the best recognition rate achieved is 71.3% for sound frame recognition. In the experiments for sound clips recognition, we have tried 8 different feature vectors, i.e. 8 different combinations of acoustic features. To recognize pure-instrument sound clips, the feature vector consisting of 40 coefficients is found to be the best. The recognition rate achieved is 97.1%. To recognize mixed-sound clips, the feature vector consisting of 17 coefficients (MFCC+PDC) is found to be the best. The recognition rate achieved is 94.7%. If average recognition rate is concerned, the feature vector consisting of 40 coefficients would be the best. The recognition rate achieved is 93.8%. Therefore, the feature vector that obtains the highest recognition rate is of 40 dimensions and consists of MFCC, PDC, Chroma-extended features, and their delta values.

摘要 I
ABSTRCT II
致謝 III
目錄 IV
圖表索引 VI
第1章 緒論 1
1.1 研究動機 1
1.2 文獻回顧 1
1.3 研究方法 5
1.3.1 歌聲與樂器聲識別系統之訓練流程 6
1.3.2 歌聲與樂器聲識別系統之測試流程 7
1.4 論文架構 10
第2章 語料準備與特徵係數擷取 11
2.1 語料簡介 11
2.2 自動標音 11
2.3 聲音片段之切割 14
2.4 音框切割與窗函數 15
2.5 梅爾倒頻譜係數估計 15
2.6 基週偵測之特徵係數 19
2.7 Chroma特徵係數與能量標準差係數 22
2.7.1 Chroma向量 25
2.7.2 Chroma之延伸特徵係數 26
2.7.3 Chroma延伸特徵係數之差分 27
2.7.4 均方根能量之標準差 28
第3章 高斯混合模型(GMM) 30
3.1 簡介 30
3.2 歌唱聲、樂器聲與混合聲之GMM訓練 32
3.3 GMM之機率計算 35
第4章 音框單位之聲音識別實驗 37
4.1 十二維MFCC之特徵係數實驗 40
4.2 MFCC加五維基週偵測特徵係數之實驗 41
4.3 加入差分值之實驗 42
4.3.1 取3音框之差分 43
4.3.2 取11音框之差分 44
4.4 Chroma之延伸特徵及差分值之識別實驗 45
4.4.1 A型Chroma延伸特徵向量 45
4.4.2 B型Chroma延伸特徵向量 47
4.5 綜合討論 48
第5章 片段單位之聲音識別實驗 52
5.1 十二維MFCC特徵之實驗 52
5.2 MFCC加五維基週偵測特徵之實驗 53
5.3 加入差分特徵值之實驗 54
5.3.1 取3音框之差分 54
5.3.2 取11音框之差分 55
5.4 Chroma延伸特徵及差分值之識別實驗 56
5.4.1 A型Chroma延伸特徵向量 56
5.4.2 B型Chroma延伸特徵向量 57
5.5 結合多種特徵向量之識別實驗 58
5.5.1 20維特徵向量 58
5.5.2 40維特徵向量 59
5.6 綜合討論 60
第6章 結論 68
參考文獻 72

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