發(fā)布時(shí)間：2018-03-14 01:37

  本文選題：灰色T型關(guān)聯(lián)度　切入點(diǎn)：改進(jìn)　出處：《數(shù)學(xué)的實(shí)踐與認(rèn)識(shí)》2017年20期 　論文類型：期刊論文

【摘要】：在音樂分類問題中,絕大多數(shù)的算法需要提取多個(gè)特征值進(jìn)行分析,工作量和復(fù)雜程度也隨之增加,并且分類太絕對(duì)化.為了降低工作量和復(fù)雜程度,采用新的方式對(duì)音樂進(jìn)行分類,即引進(jìn)灰色關(guān)聯(lián)度分析方法.現(xiàn)有的灰色T型關(guān)聯(lián)度模型均存在不足,對(duì)序列采用絕對(duì)初值化處理,并且對(duì)關(guān)聯(lián)系數(shù)計(jì)算公式進(jìn)行改進(jìn),增強(qiáng)了結(jié)果的準(zhǔn)確性和可信度.將提取出的短時(shí)能量、短時(shí)平均過零率和短時(shí)平均幅度作為音樂分類的三大特征值,對(duì)大部分音樂進(jìn)行了較為準(zhǔn)確的分類,排除率達(dá)到90.1%.而且此方法能夠體現(xiàn)出各音樂之間的關(guān)聯(lián)程度,使得分類更加人性化,這點(diǎn)具有現(xiàn)實(shí)意義.并且避免了復(fù)雜的計(jì)算過程和巨大的工作量,簡(jiǎn)化了解決問題的方式,也減少了對(duì)特征值的依賴,僅采用三種特征就達(dá)到了很好的效果.這充分反映了思路的正確性、實(shí)用性和可行性.
[Abstract]:In the music classification problem, most of the algorithms need to extract multiple eigenvalues for analysis, the workload and complexity also increase, and the classification is too absolute. In order to reduce the workload and complexity, The music is classified in a new way, that is, the grey correlation degree analysis method is introduced. The existing grey T-type correlation degree models are all deficient, the sequence is treated with absolute initial value, and the formula for calculating the correlation coefficient is improved. The accuracy and reliability of the results are enhanced. The extracted short time energy, short time average zero crossing rate and short time average amplitude are taken as the three characteristic values of music classification, and the majority of music is classified more accurately. The exclusion rate is 90.1 and this method can reflect the degree of relevance between the various music, which makes the classification more humanized, and avoids the complicated calculation process and huge workload, and simplifies the way to solve the problem. It also reduces the dependence on the eigenvalues and achieves good results by using only three features, which fully reflects the correctness, practicability and feasibility of the idea.
【作者單位】： 海南大學(xué)信息科學(xué)技術(shù)學(xué)院;
【基金】：海南省自然科學(xué)基金(117011) 海南省教育廳高�？蒲匈Y助項(xiàng)目(Hnky2017-12) 海南大學(xué)青年基金(hdkyxj201719);海南大學(xué)教育教學(xué)科研資助項(xiàng)目(hdjy1639)
【分類號(hào)】：J61;N941.5
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本文編號(hào)：1609034