基于系综经验模式分解（EEMD）的汉语共振峰检测算法
关键词 汉语 元音 共振峰检测 系综经验模式分解
毕业论文设计说明书（论文）外文摘要
Title Based on the ensemble empirical mode decomposition
(EEMD) Chinese formant detection algorithm
Abstract
Mandarin,each syllable contains a vowel or called vowel.A total of 38 vowels,where 8 is a single vowel,14 is a complex vowels,16 nasal vowels.When speaking a singal vowel,vocal organs is essentially the same,so these sound spectrogram formants position remained unchanged; complex vowel formant unlike vowels as relatively stable, while showing a continuous change dynamic characteristics;nasal vowels is [n] or [ng] ending vowels, nasal vowels are important features of the larger main vowel sounds influence mediated by both the nose and the tail by a greater impact, the latter becoming nasal vowels, the nasal vowels can cause the width and intensity of each primary vowel formants have a greater change.Thus, accurately detect the characteristic of formants in Chinese is important for Chinese signal analysis and identification.This project intends to use the ensemble empirical mode decomposition (EEMD) for Chinese language formant detection,to solve the traditional formant detection tools appear false and formant peak merger problem when extracting formants.
Keywords Chinese vowel detection of formants
ensemble empirical mode decomposition (EEMD)
目 次
1 绪论 1
1.1 课题研究背景及意义 1
1.2 目前存在的问题 2
1.3 本文研究的内容和组织安排 2
2 共振峰的定义与特征 3
2.1 共振峰的定义 3
2.2 共振峰模型的分类 3
2.3 汉语共振峰提取算法的分类 5
2.3.1 带通滤波器组法 5
2.3.2 倒谱法 6
2.3.3 LPC法 7
2.4 传统LPC法提取共振峰 8
2.4.1 LPC法的基本原理 8
2.4.2 LPC法的共振峰分析 10
2.5 本章小结 12
3 基于系综经验模式分解（EEMD）的汉语共振峰检测 13
3.1 经验模式分解（EMD）的基本原理 13
3.2 系综经验模式分解（EEMD）的基本原理 13
3.3 基于系综经验模式分解（EEMD）的汉语共振峰检测 14
3.4 本章小结 17
4 实验结果对比与分析 18
4.1 实验语音样本库 18
4.2 实验结果分析 18
4.3 实验总体对比 24
4.4 本章小结 24
5 总结与展望 26
致谢 27
参考文献 28
1 绪论
1.1 课题研究背景及意义