博碩士論文 985202030 詳細資訊




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姓名 蔡旻剛(Min-Kang Tsai)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 基於非均勻尺度-頻率圖之環境聲音辨識
(Non-uniform Scale-Frequency Map for Environmental Sound Recognition)
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摘要(中) 本論文對於環境聲音的辨識提出一個新穎的參數擷取技術稱為non-uniform scale-frequency map。對每一個frame,我們利用matching pursuit演算法從Gabor字典中選取重要的atoms。忽略phase和position的資訊,我們選擇atoms的scale和frequency建構一個scale-frequency map。在應用主成分分析和線性鑑別分析於scale-frequency map後,產生最終之特徵向量。
對於環境聲音辨識,我們執行一個區段層級的多類支持向量機(SVM)。在實驗方面,我們採用17個類別的聲音資料庫,結果顯示提出的方法能夠達到86.47% 的準確率,跟其它時頻參數的效果比較,本論文所提出之特徵參數具明顯優越性。
另外,我們對於語音情緒辨識也提出一個新穎的參數擷取技術稱為SFM descriptor。對於每一個frame,我們一樣利用matching pursuit演算法選取atom,然後建構scale-frequency map。接著我們對每一個scale-frequency map擷取descriptor參數。然後建議的SFM descriptor結合non-uniform SFM 和MFCC且送進分類器。對於語音情緒辨識,我們執行一個語句層級的多類支持向量機。在實驗方面,我們採用7個類別的情緒語音資料庫,且辨識率可以達到73.96%。
摘要(英) In this study, we present a novel feature extraction technique called non-uniform scale-frequency map for environmental sound recognition. For each audio frame, we use matching pursuit algorithm to select important atoms from the Gabor dictionary. Ignoring phase and position information, we extract the scale and frequency of the selected atoms to construct a scale-frequency map. Principle component analysis (PCA) and linear discriminate analysis (LDA) are then applied to the scale-frequency map, generating a 16-dimensional vector. In the recognition phase, a segment-level multiclass support vector machine (SVM) is performed. Experiments are carried out on a 17-class sound database, and the result shows that the proposed approach can achieve an 86.47% accuracy rate. The performance comparison between the other time-frequency features demonstrates the superiority of the proposed feature. Other, we also present a novel feature extraction technique called SFM descriptor for emotional sound. For each frame,we use matching pursuit algorithm to select atom ,then construct scale-frequency map. Next,we extract descriptor feature for each scale-frequency map, then proposed SFM descriptor combined with non-uniform SFM feature and MFCC and sent into multiclass SVM. In the recognition phase,a file-level multiclass support vector machine (SVM) is performed. Experiments are carried out on 7-class emotional sound database and the result of recognition can achieve 73.96%.
關鍵字(中) ★ 匹配追蹤
★ 非均勻尺度-頻率圖
★ 環境聲音辨識
★ 加伯函數
★ 參數擷取
關鍵字(英) ★ Gabor function
★ Nonuniform scale-frequency map
★ matching pursuit
★ feature extraction
★ environmental sound classification
論文目次 Chapter 1 Preface - 1 -
1-1 Introduction - 1 -
1-2 Motivation - 3 -
1-3 Method Construct - 4 -
Chapter 2 Literature Review - 6 -
2- 1 Audio Feature Extraction Approach - 6 -
2-2 Time Domain Feature - 7 -
2-3 Frequency Domain Feature - 9 -
2-4 Mel-Frequency Cepstral Coefficients - 10 -
2-5 Time-Frequency Feature - 14 -
Chapter 3 Support Vector Machine - 16 -
3-1 Separable Case - 16 -
3-2 Non-Separable Case - 19 -
3-3 Non-Linear Case - 21 -
3-4 Multiple Classification - 22 -
Chapter 4 Proposed Method - 25 -
4-1 Dimension Reduction Method - 25 -
4-2 MATCHING PURSUIT ALGORITHM - 27 -
4-3 Gabor Dictionary - 29 -
4-4 Non-uniform Scale Frequency Map - 31 -
Chapter 5 SFM Descriptor for Emotion Speech - 37 -
5-1 Emotional feature - 37 -
5-2 SFM Descriptor - 39 -
Chapter 6 Experiment results - 42 -
6-1Environmental Sound Database and Emotional Sound Database - 42 -
6-2 Comparison of Uniform Band and Non-uniform Band - 42 -
6-3 The Purpose of Applying PCA and LDA - 46 -
6-4 The Recognition Result of Non-uniform SFM in Different SNR Level - 50 -
6-5 Emotion Speech Recognition - 51 -
Chapter 7 Conclusion - 54 -
Reference - 55 -
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指導教授 王家慶(Jia-Ching Wang) 審核日期 2011-8-23
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