【摘要】：車(chē)輛經(jīng)過(guò)減速帶時(shí)與其在路面正常行駛時(shí)的聲信號(hào)波形明顯不同,其特征參數(shù)的提取對(duì)車(chē)輛數(shù)量、速度、類(lèi)型等的自動(dòng)判斷至關(guān)重要,聲信號(hào)包絡(luò)曲線(xiàn)對(duì)其特征參數(shù)的提取相比原始信號(hào)有諸多優(yōu)勢(shì),但傳統(tǒng)包絡(luò)提取算法在此類(lèi)交通領(lǐng)域聲信號(hào)包絡(luò)提取方面存在毛刺多、特征參數(shù)難以真正體現(xiàn)信號(hào)性質(zhì)和特征的問(wèn)題。為解決此問(wèn)題,結(jié)合車(chē)輛經(jīng)過(guò)減速帶時(shí)的聲信號(hào)特點(diǎn),提出一種基于變換步長(zhǎng)的車(chē)輛壓線(xiàn)聲信號(hào)包絡(luò)提取算法。該算法通過(guò)設(shè)置不同步長(zhǎng)遍歷信號(hào),以每個(gè)步長(zhǎng)內(nèi)的最大值點(diǎn)繪制曲線(xiàn)并與原信號(hào)波形對(duì)比,以輪廓清晰度和特征點(diǎn)提取誤差值為判斷依據(jù)實(shí)現(xiàn)聲信號(hào)包絡(luò)的有效提取。實(shí)驗(yàn)結(jié)果表明,在相同采樣點(diǎn)數(shù)條件下,所提算法比傳統(tǒng)包絡(luò)提取算法提取的包絡(luò)曲線(xiàn)輪廓更清晰、毛刺少,且特征參數(shù)提取誤差小。
[Abstract]:When a vehicle passes through a speed reducer, it is obviously different from the sound signal waveform of a vehicle when it is running normally on the road. The extraction of its characteristic parameters is of great importance to the automatic judgment of the number, speed and type of the vehicle, etc. Compared with the original signal, the acoustic signal envelope curve has many advantages over the original signal, but the traditional envelope extraction algorithm has many burrs in this kind of traffic field. It is difficult for the characteristic parameters to reflect the nature and feature of the signal. In order to solve this problem, an envelope extraction algorithm based on the transform step size is proposed to solve the problem, considering the characteristics of the acoustic signals of vehicles passing through the reducer. By setting different step sizes to traverse signals, the algorithm draws the curve with the maximum points in each step and compares with the original signal waveforms, and realizes the effective extraction of acoustic signal envelope according to the judgment of contour clarity and feature points extraction error. The experimental results show that the proposed algorithm is clearer than the traditional envelope extraction algorithm in extracting the contour of the envelope curve with less burr and less error in feature extraction under the same sampling points.
【作者單位】： 重慶交通大學(xué)信息科學(xué)與工程學(xué)院;重慶文化職業(yè)藝術(shù)學(xué)院基礎(chǔ)教育部;
【基金】：重慶市基礎(chǔ)科學(xué)與前沿技術(shù)研究專(zhuān)項(xiàng)(cstc2016jcyj A0345)~~
【分類(lèi)號(hào)】：TN911.7;U495

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1 鄒志云,張席洲,張本勇;武漢市機(jī)動(dòng)車(chē)停放特征參數(shù)調(diào)查研究[J];武漢交通科技大學(xué)學(xué)報(bào);1999年04期

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