高音譜表 的英文怎麼說

中文拼音 [gāoyīnpǔbiǎo]
高音譜表 英文
treble
  • 高 : Ⅰ形容詞1 （從下向上距離大; 離地面遠） tall; high 2 （在一般標準或平均程度之上; 等級在上的） above...
  • 音 : 名詞1. （聲音） sound 2. （消息） news; tidings 3. [物理學] （音質） tone 4. （姓氏） a surname
  • 譜 : Ⅰ名詞[書面語]1 （按類別或系統編成的書或冊子等） table; chart; register 2 （指導練習的格式或圖形）...
  • 表 : Ⅰ名詞1 （外面;外表） outside; surface; external 2 （中表親戚） the relationship between the child...
  • 高音 : [音樂] high pitch; high-pitched voice
  1. This thesis tries to update the cmdsr system to achieve the characters below : real - time, better robust, higher recognition rate, non - special - man. considering the disadvantages of traditional improved spectrum subtraction speech enhancement, this thesis proposes the theory of fuzzy spectrum subtraction based on the fuzzy theory and improved spectrum subtraction speech enhancement ; as for the difficulties of detecting the endpoint of speech signal, the thesis gives the table of initial and the improved parameters, with which we can confirm the endpoints of mandarin digit speech ; the thesis puts forward two - level digit real - time speech recognition system, the first level is based on discrete hidden markov model which is linear predictive coding cepstrum ( lpcc ) and difference linear predictive coding cepstrum ( dlpcc ), the second level is based on formant parameters ; as for the realization of hardware, the thesis depicts the realization of every part of cmdsr based on the tms320vc5402 in detail ; as for the development of software, the thesis gives the software design flow chart of cmdsr, simulates the basic theory with matlab language and gives the simulation results

    針對傳統的「改進譜相減法語音增強」參數設定單一、環境適應能力差的缺點，提出了一種利用模糊理論和「改進的譜相減法」結合的「模糊譜相減法語音增強」 ;針對語音信號端點檢測困難的特點，通過matlab模擬試驗，給出了能夠準確確定數碼語音端點的初始和改進參數表;提出了利用基於線性預測編碼倒譜參數和差分線性預測編碼倒譜參數相結合的離散隱含馬爾可夫模型進行第一級識別、利用共振峰參數進行第二級識別的兩級漢語數碼語音識別系統，在保證系統實時性的同時，實現連接漢語數碼語音識別系統識別率的提高;在硬體實現上，詳細闡述了基於tms320vc5402的連接漢語數碼語音識別系統各部分硬體設計;在軟體開發上，給出了連接漢語數碼語音識別的軟體設計各部分的流程圖，並對各部分進行了matlab模擬，並給出了模擬結果。
  2. Pitched in the first octave above the treble staff ; high

    高音調的，高八度的高音譜表上方第一個八度的；高音調的
  3. The statistic of wavelet transform coefficient algorithm can solve the periodic noise, high - energy noise and some non - gauss noise simply and effectively ; bi - spectrum can acquire more information from the original signal than power - spectrum, detect more information except from range and restrain the gauss noise. short - time speech signal can be considered as stationary and with periodic non - gauss signal, so we can make use of bi - spectrum to obtain the speech character and separate the speech and noise and detect morse telegraph signal ; complex number spectrum variance algorithm is put forward based on the deeply observing speech data, it is a new algorithm, experiment show that it is simple, effective

    統計演算法在解決周期信號、高能噪聲和高斯信號方面有獨特之處，能簡單有效提取以上噪聲的特徵;雙譜能夠提供比功率譜更多的有用信息，有效地檢測信號幅度之外的其它信息，並能有效抑制高斯噪聲，短時語音信號一般認為是平穩且有一定的周期性的非高斯信號，因而可以利用雙譜來提取語音信號特性並實現信噪分離;復數譜方差演算法是在對語音信號進行深入觀察和分析的基礎上而提出來的一種全新的語音特徵提取方法，此方法簡單而有效的提取了語音、噪聲的特徵以及檢測莫爾斯信號，基於實驗表明，該演算法取得了很好的效果。
  4. There are difficulties in noisy speech recognition, especially low signal - to - noise rations are more difficult. this paper describes briefly six methods for speaker - dependent noisy speech recognition isolated words. they are lpc prediction error method, one - side auto - correlation sequence lpc, acoustic front end processing, canonical correlation based on compensation method, combination of features method and increase of poles method. the experimental results show that all the six techniques can improve effectively noisy speech recognition, and the best noisy speech recognition rate is above 80 % when snr 0db

    它們是：線性預測誤差法，單邊自相關線性預測法，語音前端聲學處理法，正則相關分析的譜變換補償方法，特徵綜合法和同模極點增加法。實驗結果表明，這6種方法都有效地提高了噪聲環境中語音識別率，其中較好的方法在強噪聲環境中信噪比為0db的語音識別率達到80 %以上，為信噪比較低的噪聲環境中自動語音識別展現了美好前景。
  5. One is using the autocorrelation function to detect the speech terminal, the other is using the coefficients based on the one - sided autocorrelation sequence to replace the original speech signal and then extract the speech feature to recognize. isolated word recognition experiment based on dtw shows : it can reduce the disturbance of noise effectively and get the high recognition rate. it is of great advantage to apply when snr signal to noise rate is low

    對含噪語音在自相關域上進行處理，以其自相關函數值為參數進行端點檢測，以基於單邊自相關序列的lpc倒譜系數作為語音的特徵參數進行語音識別，實驗表明：這種方法較好地消除了噪聲對語音信號的干擾，並獲得了較高的識別率。
  6. Speech enhancement experimental results show that this proposed method effectively improves the speech quality and reduces the musical noise

    語音增強實驗結果表明，該改進譜減演算法能有效地提高增強效果，更好地抑制音樂噪聲，提高語音質量。
  7. Dynamic mapping algorithm is also illustrated in details. through the computer simulation to some real short - time voice signal samples using matlab language. the result shows that the recognition efficiency using cepstrum coefficients mapping is better than what made by lpc mapping

    實驗結果表明，與採用lpc特徵相比，採用lpc倒譜特徵和動態匹配演算法進行短時語音識別，會有較高的識別率；對不同語音信號有特徵空間離散度大、易於確定判別門限的特點。
  8. Noteheads may be black ( a circular or oval shape filled in ) or white and are used on the staff to indicate pitch

    符頭可以是黑色的（圓形或卵形）或白色的，用在五線譜上以表明音高。
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