本文關(guān)鍵詞：基于鳴聲的鳥類智能識別方法研究　出處：《西北農(nóng)林科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 鳥類識別 鳴叫聲 鳴唱聲 MFCCA 雙重GMM

【摘要】：鳥類是濕地野生動物中最具代表性的類群，是濕地生態(tài)系統(tǒng)重要組成部分，也是監(jiān)測濕地環(huán)境質(zhì)量重要的生物指標(biāo)。鳥的種類確定對濕地生物多樣性和生態(tài)平衡提供了重要的依據(jù)。鳥類的鳴聲和形態(tài)特征一樣，具有物種的特性，是鳥類重要的生物學(xué)特征，也是識別鳥類的重要依據(jù)。 本文針對我國經(jīng)濟開發(fā)和環(huán)境保護的矛盾突出，濕地資源遭受嚴(yán)重破壞的問題，在分析現(xiàn)有聲音識別技術(shù)原理與系統(tǒng)結(jié)構(gòu)的基礎(chǔ)上，研究基于鳴聲的鳥類智能識別方法，為實現(xiàn)濕地鳥類監(jiān)測和遷徙規(guī)律提供技術(shù)支持。本文的主要工作和結(jié)論如下： （1）在分析當(dāng)前已有研究成果的優(yōu)點與不足的基礎(chǔ)上，根據(jù)鳥類鳴聲的特點，提出了分別處理鳴叫聲和鳴唱聲，采用雙重高斯混合模型的設(shè)計方案。 （2）綜合考慮聲音樣本的多寡、地域的合理性、科目的差異和鳴聲類型等因素，選擇以陜西地區(qū)常見的黃臀鵯、矛紋草鹛、北紅尾鴝、綠背山雀、方尾瀇、紅嘴相思鳥、黃喉濦和淡尾瀇鶯8種鳥類為研究對象，收集其鳴聲，并使用Goldwave軟件對聲音樣本進行除噪、裁剪等預(yù)處理。 （3）在分析Mel倒譜系數(shù)（MFCC）理論基礎(chǔ)和實現(xiàn)方案基礎(chǔ)上，對MFCC進行了改進，提出一種新的特征參數(shù)MFCCA，并對其在正確識別率和識別效率上進行實驗。實驗結(jié)果表明，MFCCA特征參數(shù)具有更好的靈活性和魯棒性，更適合作為鳥類鳴聲的特征參數(shù)。 （4）根據(jù)鳥類鳴聲分為鳴叫聲和鳴唱聲的特點，打破以前采用單高斯混合模型（GMM）的傳統(tǒng)，分別對每種鳥類建立鳴叫聲GMM模型和鳴唱聲GMM模型，并進行正確識別率和識別效率的實驗。結(jié)果表明，當(dāng)采用雙重GMM模型時，識別效果最好，并且識別效率影響不大。并討論不同階數(shù)對與GMM模型的影響，結(jié)果表明當(dāng)GMM模型的階數(shù)為32時識別效果最好。 （5）選取陜西地區(qū)50種鳥類，提取MFCCA特征參數(shù)，，建立雙重GMM模型，并設(shè)計測試軟件進行測試，結(jié)果表明，本文提出的鳥類智能識別方法的正確識別率能達到91.52%。
[Abstract]:Birds are the most representative group of wetland wildlife and an important part of wetland ecosystem. It is also an important biological index to monitor wetland environmental quality. Bird species determination provides an important basis for wetland biodiversity and ecological balance. It is an important biological characteristic of birds, and it is also an important basis to identify birds. In view of the contradiction between economic development and environmental protection in China and the serious damage to wetland resources, this paper analyzes the principle and system structure of existing sound recognition technology. In order to provide technical support for the monitoring and migration of wetland birds, the intelligent recognition method based on song sound is studied. The main work and conclusions of this paper are as follows: 1) on the basis of analyzing the advantages and disadvantages of the existing research results, and according to the characteristics of bird song, the design scheme of dealing with the singing sound and singing sound separately and adopting the mixed model of dual Gao Si is put forward. (2) considering the number of sound samples, the rationality of region, the difference of subjects and the type of song, the author chooses the common yellow-breasted Bulbul, spear-striped Babbler, Northern red-tailed robin, green-backed tit, square tail and so on. Eight species of birds, red mouth acacia, yellow larynx and light tail warbler, were collected, and the sound samples were pretreated with Goldwave software. 3) based on the analysis of Mel cepstrum coefficient and its implementation scheme, the MFCC is improved and a new characteristic parameter MFCCA is proposed. Experiments on the correct recognition rate and recognition efficiency show that MFCCA feature parameters have better flexibility and robustness, and are more suitable as the characteristic parameters of bird song. (4) according to the characteristics of birds' singing and singing, the tradition of single Gao Si mixed model was broken. GMM model and GMM model were established for each bird, and the correct recognition rate and recognition efficiency were tested. The results showed that the recognition effect was the best when using double GMM model. The effect of different order on GMM model is discussed. The results show that the recognition effect is the best when the order of GMM model is 32. 5) 50 species of birds in Shaanxi are selected, the characteristic parameters of MFCCA are extracted, the dual GMM model is established, and the test software is designed for testing. The results show that. The correct recognition rate of the intelligent bird recognition method proposed in this paper can reach 91.52%.
【學(xué)位授予單位】：西北農(nóng)林科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN912.34

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