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姓名	林昶宏(Chang Hong Lin)  查詢紙本館藏	畢業系所	資訊工程學系
論文名稱	強健性聲音事件辨識之研究 (A Study on Robust Sound Event Recognition)
相關論文	★ Single and Multi-Label Environmental Sound Recognition with Gaussian Process ★ 波束形成與音訊前處理之嵌入式系統實現 ★ 語音合成及語者轉換之應用與設計 ★ 基於語意之輿情分析系統 ★ 高品質口述系統之設計與應用 ★ 深度學習及加速強健特徵之CT影像跟骨骨折辨識及偵測 ★ 基於風格向量空間之個性化協同過濾服裝推薦系統 ★ RetinaNet應用於人臉偵測 ★ 金融商品走勢預測 ★ 整合深度學習方法預測年齡以及衰老基因之研究 ★ 漢語之端到端語音合成研究 ★ 基於 ARM 架構上的 ORB-SLAM2 的應用與改進 ★ 基於深度學習之指數股票型基金趨勢預測 ★ 探討財經新聞與金融趨勢的相關性 ★ 基於卷積神經網路的情緒語音分析 ★ 運用深度學習方法預測阿茲海默症惡化與腦中風手術存活
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摘要(中)	近年來，環境聲音辨識在家庭自動化應用中已成為一個新的研究主題。在家庭自動化系統中，正確辨識環境中的聲音是執行任務的基礎。然而，真實環境中有外在干擾會導致辨識率低落，例如目標聲音與其他聲音同時出現，或是有環境噪音的干擾。為了處理這兩個問題，在此篇論文中，我們共提出了三套強健性處理方法。我們首先提出了一套混和聲音辨識方法來處理聲音同時出現的問題。對於環境噪音的問題，本論文採用兩種方法來移除噪音的影響。第一種方法是先移除收到訊號中的噪音後，再擷取特徵參數，稱作聲音強化。第二種方法則是在移除噪音的同時也擷取特徵參數，稱作強健性特徵參數擷取。在此篇論文中，對於聲音同時出現的問題，我們提出一個基於無線感測網路下的混和聲音驗證方法。此架構包括基於無線感測網路的聲音分離以及聲音驗證技術。在有噪音的環境下，對於聲音強化的方式，本論文提出了快速子空間聲音增強演算法濾除背景雜訊。對於強健性特徵參數擷取的方式，本論文提出了一套基於非均勻尺度-頻率圖的參數擷取方法。實驗數據顯示出，在有聲音同時出現或是有環境噪音下，我們提出的三種方法與基準方法相比，我們的系統都具有更高的辨識率。
摘要(英)	In recent years, environmental sound recognition has become a new research topic in home automation. In home automation systems, the sound recognized by the system becomes the basis for performing certain tasks. However, there are various disturbances which may cause recognition system to fail in real world applications. For example, a target source is mixed with another sound due to simultaneous occurrence, or the sound received by the applications is exposed to background noise. To resolve these two issues, we totally propose three robust processing methods in this dissertation. We firstly propose a mixed sound verification method to deal with simultaneous occurrence of sounds. For the problem of background noise, this dissertation adopts two approaches to reduce the impact on recognition. The first approach is sound enhancement, which suppresses the noise of received sound before feature extraction. The second approach is to simultaneously remove noise and extract feature (implements feature extraction and denoising simultaneously), called robust feature extraction. To handle the problem of simultaneous occurrences of multiple sounds, this study proposes a framework, which consists of sound separation and sound verification techniques based on a wireless sensor network (WSN). For the problem of reducing noice from the input audio, we propose a fast subspace based sound enhancement method to filter background noise on signal subspace. For the approach of robust feature extraction, we proposed a novel feature extraction approach called nonuniform scale-frequency map for environmental sound recognition. Furthermore, the experimental results demonstrate the robustness and feasibility of the three proposed systems are superior to baseline systems.
關鍵字(中)	★ 強健性聲音事件辨識 ★ 混和聲音事件驗證 ★ 音訊增強 ★ 強健性特徵參數擷取	關鍵字(英)	★ Robust Sound Event Recognition ★ Mixed Sound Event Verification ★ Sound Enhancement ★ Robust Feature Extraction
論文目次	摘要 I Abstract II List of Figures V List of Tables VI Chapter 1 Introduction 1 Chapter 2 Related Work 5 2.1 Sound Recognition 5 2.2 Mixed Sound Separation 7 2.3 Sound Enhancement 9 2.4 Robust Feature Extraction 11 Chapter 3 Mixed Sound Event Verification on Wireless Sensor Network 13 3.1 Introduction 13 3.2 Mixed Sound Event Verification on Wireless Sensor Network 14 3.3 Mixed Sound Separation 16 3.4 Sound Verification 24 3.5 Experimental Results 27 3.6 Summary 33 Chapter 4 Robust Environmental Sound Recognition Using Fast Subspace Based Noise Suppression 34 4.1 Introduction 34 4.2 System Overview 34 4.3 Fast Subspace Based Noise Suppression 35 4.4 Wavelet Subspace Based Features 42 4.5 Experimental Results 45 4.6 Summary 50 Chapter 5 Gabor-Based Nonuniform Scale-Frequency Map for Environmental Sound Recognition 51 5.1 Introduction 51 5.2 System Overview 52 5.3 Proposed Scale-Frequency Map 53 5.4 Dimensional Reduction of Scale-Frequency Maps 58 5.5 Experimental Results 60 5.6 Summary 69 Chapter 6 Conclusion and Future Work 70 6.1 Conclusion 70 6.2 Future Work 71 Bibliographies 73 Publication List 82
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指導教授	王家慶(Jia-Ching Wang)	審核日期	2014-8-27
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