博碩士論文 100522603 詳細資訊




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姓名 西雅恩(Ernestasia Siahaan)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 Single and Multi-Label Environmental Sound Recognition with Gaussian Process
(基於高斯程序之單一及多重標籤環境聲音辨識)
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摘要(中) Sound recognition applications play an important role in various aspects of human life, with research efforts being put into recognition systems of different kinds of sounds, i.e. speech, music, and environmental sounds. This thesis deals with the problem of environmental sound recognition, as it is a highly interesting part of sound recognition research due to the range of potential applications that benefit from it. We address two prominent parts of a recognition problem that hold an important role in delivering high performance in terms of recognition accuracy, i.e. the feature extraction and classification part.
We proposed to use features extracted from the wavelet domain of a signal, as it is considered to provide better analysis of environmental sound audio signals. We extract the wavelet packet decomposition of an audio signal, and derive the signal’s spectral centroid, sparsity, flatness and spread using the wavelet nodes, as well as a set of wavelet-based cepstral coefficients. In addition, we propose the use of a set of histogram features calculated from the wavelet based features. We compare the performance of the different feature sets in our experiments.
In the classification part of the system, we propose the use of Gaussian Process based classifier. We propose a multiple kernel approach, in which we combined the linear kernal and probability product kernel to present two different kinds of similarity notion from our data in the learning algorithm. We show the probability product kernel between two kernel density estimations, and then combine it with the linear kernel using a weighted linear combination approach, and multiplication approach.
Two kinds of recognition problems are observed in this thesis, i.e. singular and multi-label problems. Through our experiments, we show that the proposed features and classification approach yielded satisfying recognition results in both singular and multi-label classification. Moreover, the use of multiple features in multiple kernel in a Gaussian Process further improved the system performance.
摘要(英) 聲音辨識的應用在人類生活中許多方面扮演了重要的角色,而現在對於聲音辨識的研究主要在不同種類聲音的辨識系統上,例如:語音、音樂、環境聲音。本篇論文討論環境聲音辨識的問題,因為環境聲音辨識的研究有廣泛的潛在性應用,因此它在聲音辨識的領域中是個十分令人感興趣的部分。我們要解決兩個在辨識問題中扮演提高辨識率的重要角色的部分,分別是特徵值選取與分類方法。
我們使用從訊號的小波域中選取的特徵值,因為這些特稱值提供了更好的環境聲音訊號的分析。我們取出聲音訊號的小波包分解以及一組基於小波轉換的倒頻譜係數,並且用小波節點推導出訊號的頻譜中心、稀疏性、平整度及分散度。此外,我們使用從基於小波的特徵值計算出來的一組直方圖特徵值。我們在實驗中比較不同組特徵值的效果。
在辨識系統的分類方法部分,我們提出基於高斯程序的分類器。我們提出一個多重核心的方法,此方法是結合線性核心和機率乘積核心來表示我們在學習演算法中資料的兩種相似性概念。我們描述了在兩種核心密度估計中的機率乘積核心,並且用加權線性組合與乘法方法將機率乘積核心與線性核心結合。
本篇論文敘述兩種辨識問題-單數標籤與多重標籤問題。經由實驗,我們證明了我們提出的特徵值以及分類方法滿足單數標籤與多重標籤分類問題的辨識結果。此外,在高斯程序中,多重特徵值在多重核心中的使用進一步提升了辨識系統的效能。
關鍵字(中) ★ 高斯程序
★ 環境聲音辨識
關鍵字(英) ★ Gaussian Process
★ Environmental Sound Recognition
論文目次 摘要       i
ABSTRACT ii
ACKNOWLEDGEMENTS iii
LIST OF FIGURES vi
LIST OF TABLES vii
I. INTRODUCTION 1
II. RELATED WORK 5
2-1. Environmental Sound Recognition 5
2-1-1. Feature Selection 5
2-1-2. Challenges and Applications 9
2-2. Multi-Label Classification Problem 11
2-3. Gaussian Process for Classification 14
2-4. Kernel Methods 16
2-4-1. Multiple Kernel 17
III. METHODOLODY 20
3-1. System Overview 20
3-2. Feature Extraction 21
3-3. Multiple Kernel for Gaussian Process Classification 24
3-3-1. Multiple Kernel for Multi Features 28
3-4. Multi-Label Sound Recognition 28
3-4-1. Sound Recognition from Continuous Audio Stream 29
3-4-2. Mixed Sound Recognition 30
3-5. Evaluation 31
IV. EXPERIMENT AND RESULTS 33
4-1. Singular Sound Event Classification 33
4-1-1. Comparison of Feature Extraction and Classification Approaches 34
4-1-2. Test of Robustness 38
4-2. Multi-Label Environmental Sound Recognition 39
4-2-1. Continuous Audio Stream Test Case 39
4-2-2. Mixed Sound Test Case 41
V. CONCLUSION 43
BIBLIOGRAPHY 44
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指導教授 王家慶(Jia-Ching Wang) 審核日期 2013-8-14
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