mfcc 中文意思是什麼
mfcc
解釋
編解碼演算法數-
( 3 ) study the segmentation and recognition of audio frequency signal. audio signal can be divided into segments based on zero - crossing rate. ( 4 ) a audio recognition arithmetic based on mfcc is proposed
音頻信號的處理作為項目的一部分，根據要求實現了對單一音頻信號的識別，用vc6 . 0來實現。 -
( 2 ) study the character of audio signal. analyze the zero - crossing rate and mfcc. a mean mel coefficients is proposed, it can be used to recognized different audio signal
通過對mfcc系數進行分析，均值mfcc系數作為音頻特徵，採用動態時間規整識別演算法，能夠對單一音頻進行識別，對已有數據源進行測試，有較高的識別率。 -
In this thesis, first we analyzed and designed a traditional continued speech recognition system, which based on hmm and mfcc speech features. then we researched some noise robust technologies based on that system
本論文首先分析並實現了一個以mel頻率倒譜系數（ mfcc ）作為語音特徵，基於隱馬爾可夫模型（ hmm ） ，針對連續數字串識別任務的基本連續語音識別系統。 -
The first part of the paper is designing the testing project for grounding resistance and insulation resistance in a new way. using 16bits ad converter with programmable control amplifier replaced the way which used changing resistance to change measure range. lt is not only improved testing precision and develop the system expediently, but also reduced the area of the circuit boardwith the new way. in order to make the electric implement safety testing system have upstanding expansibility, the software and hardware of the system adopted the modularization design. adopted mcu atmegal28 as a master mcu which control mmi, realtime clock and communication with slaver mcu. atemga8 as the slaver mcu to realize testing function. so it is easy to add or reduce the testing project. the testing implement system has been developed successfully, and the comments for the system is that it has high precision, high expansibility and easy maintain. but considering the electric implement system should have intelligence and humanity abi lity. so this paper bring forward a scheme of electric equipment safety testing embedded system with speech control. after introduce the basic theory of speech recognition, the paper expatiate the characters of this system. the system is a noise conditon, not special people, small glossary, insulation word system. with these characters design the speech recognition as fellow. utilizing cross zero ratio and short energy to ensure jumping - off point and end point ; adopting mfcc as the character parameters of speech recognition ; the character parameters than be recognized by dtw. in order to ensure the credibility of this project, first realized by matlab in computer
在介紹了語音識別的基本原理后，闡述了本系統的特點：本系統是一個噪聲環境下非特定人、小詞匯量、孤立詞的語音識別系統。根據本系統的這些特點設計了如下語音識別方案：利用過零率和短時能量相結合的方式確定語音端點；採用mel頻率倒譜系數（ mfcc ）作為語音識別的特徵參數；得到的特徵參數最後通過動態時間規整（ dtw ）的模式識別方法進行識別。為了確保本系統實現方案的可靠性，首先通過計算機利用matlab軟體來模擬，在演算法模擬實現后又進一步增加環境的復雜性：加上較大的環境噪聲、突發性的噪聲等，再通過修改參數、修改參考模板、兩級識別等各種提高語音識別精度的方法來提廣東工業大學工學碩士學位論文高識別率。 -
On the importance of components of the mfcc in speech and speaker recognition
語音識別和說話人識別中各倒譜分量的相對重要性 -
Considering system security, we adopt mfcc to recognize password and lpcc to represent speaker track dynamic movement. the double decrees enable it applying in high secret situations. the system has many merit such as the quick operation velocity, easy model update, less calculate quantity and low error rate
本文考慮到系統的安全性，採用美爾倒譜系數識別密碼，線性預測倒譜差分識別說話人聲道動態變化的雙重判決方法，為系統應用在高度機密場合提供了可能，具有運算速度快，模板更新容易，計算量小，差錯率低等優點。 -
The analysis of the relative importance of components of mfcc for both speech recognition and speaker recognition using dtw recognizer in various noise environments are given. for english digit and under the euclidean distance definition, the experiment results show cepstral components from
採用增減特徵分量的方法研究了mfcc各維倒譜分量對說話人識別和語音識別的貢獻。使用dtw測度，在標準英文數字語音庫上的實驗表明，最有用的語音信息包含在mfcc分量 -
The paper involves two parts of closed - set text - independent speaker identification systems in noise. firstly, the paper introduces shortcomings of the parameters, such as lpcc, mfcc etc. so sub - band energy cepstral feature parameters based on multi - rate sub - band processing and teager energy operator are described. while the paper discusses the use of rayleigh cepstral liftering to weigh sub - band cepstral coefficients so as to emphasize speaker personality information
首先本文介紹了各種特徵提取的方法，如lpcc 、 mfcc等，在分析了它們的缺點的基礎上，本文根據人耳頻率子帶獨立識別的特性和能量運算元，提出了子帶能量倒譜特徵參數，並用提出的瑞利倒譜提升進行加權，突出說話人的個性信息。 -
The theory of lpc and mel - scaled cepstrum analysis is introduced in this dissertation and how to extract lpcc and mfcc is elaborated
詳細介紹了線性預測分析（ lpc ）和mel倒譜分析的原理及其具體實現過程。 -
The paper discussed the bandpass filters analysis method and the technology of linear prediction code ， then reduced the lpcc and the mfcc parameters
本文還介紹了語音信號分析方法中的濾波器組分析方法和線性預測編碼技術，並推導了lpcc參數和mfcc參數。 -
According to commonly steps of speech recognition, the key methods of speech recognition is discussed : a ) firstly, preprocessing and feature extraction in speech recognition is studied. we studied two important speech analysis methods and extracted two key features for speech recognition : mfcc and lpcc. on the base of the research we improve the algorithm and experiment with new method
論文根據語音識別的一般流程，主要針對語音識別系統的關鍵技術進行探討： （ 1 ）首先對語音信號的預處理和特徵提取問題進行討論。分析了當前最常用的兩種特徵參數， mfcc和lpcc ，在此基礎上對語音識別系統預處理和特徵提取作了一些改進，並給出相應的實驗驗證。 -
After analyzing the result of the experiment and the feature for text - independent recognition, we make a solution to the defects of the system. then long - time spectrum parameter and lpcc or mfcc are blended to be studied in detail to make the feature parameter. using these technologies, a high recognition rate was made
針對單獨使用lpcc或mfcc應用於說話人識別中存在識別率還不夠高的問題，分析了原因，然後又對長時頻譜特徵進行了研究，並把長時頻譜特徵分別與lpcc和mfcc結合起來共同應用於說話人識別試驗，從而在一定程度上減少了單獨使用lpcc或mfcc運用於說話人識別中存在的問題，提高了系統的識別性能，取得了較好的效果。 -
The simulation result show, this speech enhancement system design method, not only realizes simply, but also saves the running time, the speech enhancement effect is good. thirdly, this article discusses a new method of speech enhance based on neural network. mfcc coefficient of the speech signal can be picked up under noise and
用於bp神經網路的訓練和學習，利用神經網路系統具有非線性映射和自學習，能夠用於噪聲信號的非線性建模的能力，獲取信號的最佳估計，克服信號處理中存在的不確定性，最終達到語音信號消噪和提高可懂度的目的。
分享友人