評估以聲音偵測為基礎的行人專用來車預警系統設計

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李芷融(Chih-Jung Lee) 查詢紙本館藏

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通訊工程學系在職專班

論文名稱

評估以聲音偵測為基礎的行人專用來車預警系統設計
(Evaluation for designing audio-based early warning system of vehicle approaching event for improving pedestrian′s safety)

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摘要(中)

近年運動風氣興起，許多運動愛好者在郊區跑步或健走時亦習慣配戴耳機聆聽智慧型行動電話上播放的音樂。因聽覺封閉或分心而降低對週遭環境的感知，可能增加了與路上車輛發生事故的機會。
為了改進行人在此情境下的安全，本論文提出了一個簡單的系統設計並評估，以便將來在智慧型行動裝置平台上實現偵測後方來車的預警系統。
環境中的聲音將可由與行動裝置連接的指向性麥克風來收音，並經過數位訊號處理的方式取出短時間長度的音框內基本的聲音特徵：RMS(Root Mean Square)、ZCS(Zero Crossings)、SPC(Spectral Centroid)、SPR(Spectral Rolloff)。並將所取的得聲音特徵輸入至不同的機器學習分類器：kNN(K Nearest Neighbor)、MLP(Multi Layer Perceptron)、Decision Tree、Random For-est做分類，判斷該音框內是否存在來車的事件。
本研究呈現系統的辨識率及可行性，也檢視了不適用的情境。

摘要(英)

More and more people tend to carry smart phones and wear headphones or headsets, listening to the music while they are jogging or walking in the suburb area. This behavior could bring distraction or temporarily losing the hearing of environmental background and cause accident to happen.
This work proposes a simple design for audio-based early warning system of vehicle approaching event for improving pedestrian’s safety and gives evaluation.
Sound signals were collected by an external directional microphone connected to the smart phone. Multiple feature techniques like root mean square, zero crossings, spectral centroid, and spectral rolloff were applied on the short-time frames of audio samples. Multiple machine learning classifiers like K Nearest Neighbor, Multi-layer Perceptron, Decision Tree and Random Forest were applied to classify the audio frames to detect vehicle approaching sound.
The results showed the accuracy and the feasible of the system, also point out the circumstance can’t be applicable.

關鍵字(中)

★ 車輛接近
★ 聲音偵測
★ 機器學習

關鍵字(英)

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 x
第一章緒論 1
1-1 研究背景 1
1-2 研究目的與動機 2
1-3 論文架構 4
第二章文獻回顧及研究方法 5
2-1 文獻回顧 5
2-2 研究方法 5
第三章聲音特徵截取 12
3-1 Root Mean Square 12
3-2 Zero Crossings 12
3-3 Spectral Centroid 12
3-4 Spectrol Rolloff 13
3-5 都卜勒效應 15
第四章機器學習 16
4-1 K-近鄰演算法(K Nearest Neighbors algorithm)17
4-2 多層感知器(Multilayer Perceptron) 18
4-3 決策樹(Decision Tree) 19
4-4 隨機森林(Random Forest) 20
第五章實驗與分析討論 21
5-1 特徵統計分佈觀察 21
5-2 特徵趨勢觀察 26
5-3 實驗流程 28
5-3-1 實驗一 29
5-3-2 實驗二 30
5-3-3 實驗三 31
5-3-4 實驗四 33
5-3-5 實驗五 36
5-4 實驗結果討論 37
5-4-1 特徵縮放的影響 38
5-4-2 記億體限制 38
5-4-3 不平衡類分佈的影響 38
第六章結論與未來展望 39
參考文獻 40
附錄一 kNN之k值於實驗三、四、五組合結果 43
附錄二實驗二 Decision Tree輸出圖型 44

參考文獻

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指導教授

張寶基

審核日期

2017-7-26

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