臺灣博碩士論文加值系統

時頻分佈函數能精確地將訊號映射至二維的時間頻率域，故近年來在雷達、聲納、
地震震源探勘及水中聲學等非穩態訊號的範疇中應用極廣。目前的高解析時頻分佈皆以柯漢家族時頻分佈函數結構為基礎,
但時頻分佈函數由雙線性特性所構成，因而會產生類似雜訊的交關項因子，有效的消除交關項，
且讓主要訊號更加集中是現今努力的目標。本論文比較各類的核心函數對其訊號解析度的優劣點，
以提高時頻訊號解析能力。訊號經過時頻分析前置處理後，再利用KL展開特徵萃取，
由於隨機訊號主要能量若集中於主要的特徵向量，則依據此主要特徵向量作為訊號間辨識的特徵,
當訊雜比為10dB時, 此系統的辨識率可達到百分之80。

The time-frequency distribution(TFD) can accurately transform signals into two-dimensional
time-frequency domain. It has extensive application in the nonstationary fields of sonar,
seismology and underwater acoustic. Most of the current high resolution time-frequency
methods are based on the structure of the Cohen time-frequency distribution. However,
since the time-frequency distribution has the bilinear property which will produce the
cross terms factor treated it as the noise. It is important to sharp the signal and
reduce the cross term efficiently. In this thesis, we compare the signal resolution of different
kind kernel function, and try to find a way to promote the ability of time-frequency resolution.
The received signal will be analyzed with time-frequency analyzed first, and the eign vector of
analysis signal will be extracted by Karhunen-Loeve expansion. The eign vector will be compared
with database. The probability of recognition can reach 80 percent at SNR=10 dB.

第一章 序論
1.1 ~ 背景簡介
1.2 ~ 研究背景與動機
1.3 ~ 各章節內容概述
第二章 時頻分佈函數
2.1 ~ 傅立葉轉換
2.2 ~ 短時距傅立葉轉換
2.3 ~ 時頻分佈函數
2.4 ~ 時頻分佈函數的種類
2.5 ~ 時頻分佈函數之性質
2.6 ~ 離散時頻分佈函數
2.7 ~ 錐形時頻分佈函數
2.71 ~ 定義
2.72 ~ 特殊性質
2.8 ~ 可適性錐形時頻分佈函數
2.81 ~ 可適性的方法
2.82 ~ 可適錐形時頻分佈函數
2.9 ~ 各類型的核心函數分析比較
第三章 Karhunen-Loeve展開於時頻分析訊號辨識
3.1 ~ 時頻分佈函數於語音訊號分析
3.2 ~ Karhunen-Loeve展開(Expansion)
3.3 ~ 選取較精確特徵向量及臨限值的判定
3.31 ~ 選取較精確特徵向量
3.32 ~ 臨限值的判定
3.4 ~ 偵測率
第四章 使用者搜尋介面之設計
4.1 ~ 資料庫系統存取方法
4.2 ~ 系統作業流程
4.21 ~ 系統架構
4.22 ~ 介面操作介紹
第五章 結論及未來研究方向

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