目前，耳聲傳射在臨床醫學的應用上，主要為聽力量測。然而，從過去瞬態誘發耳聲傳射(TEOAE)的分析結果，發現同一健康耳在不同量測時間具有一致性及穩定性，可能適合成為生物辨識之工具。本論文研究在能有效的量測健康耳之瞬態誘發耳聲傳射的情況下，分析脈衝響應之後半段訊號是否含有同步自發性耳聲傳射(SSOAE)，並從其頻譜及ConceFT觀察是否存在數個特定頻率且穩定震盪的SSOAE。初步觀察後發現不同日期的SSOAE頻譜似乎能保有每個人的獨特性。於是本論文收集19位受測者的耳聲傳射，其中20隻耳朵含有SSOAE，約52.6 %。所以，在特徵萃取上，本研究嘗試開發包含TEOAE及SSOAE之綜合判斷機制，並以K個最近鄰居法(KNN)應用於身分辨識。適當地增添SSOAE作為特徵後，本研究在僅量測15秒的耳聲傳射，其中5位之雙耳皆無SSOAE，身份辨識率約84 %；另外14位皆至少一耳含SSOAE，其辨識率達100 %。另外，利用主成份分析將特徵維度降至17維，整體身份辨識率仍可達97.89 %。未來期望同時量測雙耳之耳聲傳射，並完成為時五秒之量測辨識系統。

Currently, otoacoustic emissions (OAEs) are applied to hearing detection in the clinical medicine. However, the previous analyses show that the transient evoked otoacoustic emission (TEOAE) is also suitable for biometric identification thanks to the consistency and stability of TEOAE across each measurement from the same healthy ear. Under the circumstances that the TEOAE can be effectively measured from the healthy ears, we analyze the second half of the impulse response and examine whether the synchronized spontaneous otoacoustic emissions (SSOAEs) existed. SSOAEs are several weak signals continually oscillating at certain frequencies. We found that SSOAE spectrum seems to preserve their uniqueness at each measurement. Collected from 19 subjects, TEOAEs were measured from both ears of 19 subjects, SSOAEs existed in approximately 52.6% of them (20 over 38 ears). This research is dedicated to developing a general detection scheme that can extract TEOAE and SSOAE as features, and thus realize person identification by utilizing the k-nearest neighbors (KNN) algorithm. Under the condition that measurements only last 15 seconds for the individual, the person identification rate is about 84% for those who lack SSOAEs (5 over 19 subjects), while the person identification rate rises to 100% after adding SSOAEs for the rest (14 over 19 subjects) whose SSOAEs existed in at least one ear. Even if we reduce the feature size to 17 dimensions by principal component analysis (PCA), the person identification rate still remains 97.89 %. In the future, it seems possible to automate all the processes for binaural measurements so that personal identification can be completed in 5 seconds for certain applications.

摘要 I
Abstract II
目錄 III
圖目錄 V
第一章 緒論 1
1.1 耳聲傳射簡介 1
1.2 研究動機 2
1.3 文獻回顧 3
1.4 章節介紹 4
第二章 實驗設備與訊號擷取流程 5
2.1 實驗設備與量測環境 5
2.2 實驗設計與流程 6
第三章 訊號分析方法與結果 8
3.1 訊號分析前處理 8
3.1.1 框取平均(frame averaging) 8
3.1.2 框取中位數(median framing) 13
3.2利用ConceFT分析瞬時頻率 15
3.3機器學習演算法 18
3.3.1 k個最近鄰居法(k-nearest neighbors, KNN) 19
3.3.2 主成份分析(principal component analysis, PCA) 20
第四章 實驗結果分析與討論 21
4.1資料庫建立與訊號分析 21
4.1.1分析受測者TEOAE訊號之結果 21
4.1.2分析受測者SSOAE訊號之結果 23
4.2統計分析與驗證 27
4.2.1統計分析 27
4.2.2 TEOAE與SSOAE訊號之頻譜比較 28
4.3耳朵辨識 29
4.3.1耳朵辨識 30
4.3.2判斷SSOAE訊號之耳朵辨識 32
4.3.3利用PCA降維之耳朵辨識 34
4.4身份辨識 35
4.4.1身份辨識 35
4.4.2判斷SSOAE訊號之身份辨識 36
4.4.3利用PCA降維之身份辨識 38
4.5實驗結果討論 39
第五章 結論與未來發展 40
參考文獻 41
附錄 45

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