果蝇鸣声特征提取及人工神经网络分类研究
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摘要
昆虫与人类的关系十分密切，它们直接或间接地影响着人类的生活。昆虫以各种行为发出的声音作为特定的交流方式，不同的行为发出的鸣声其意义也有所不同。研究其鸣声，分析鸣声产生的原因和鸣声特征，比较种群间的异同，对于掌握昆虫的活动规律、监测和控制害虫具有重要意义。利用昆虫鸣声进行分类，对那些形态上难以区分的近缘种或疑难种及种下分类十分有利。
本文对同种不同品系果蝇飞行时翅振产生的鸣声进行了采集、分析研究。
实验中所用的果蝇均为黑腹果蝇，来源于陕西师范大学生命科学学院。共有同种下的3个品系，分别为Canton-Special(Canton-S)品系和标号为18号、22号的突变型果蝇。采集声音前，先将3个品系果蝇的雌、雄人工分离，然后分别对雌、雄果蝇飞行翅振鸣声进行采集。采集的鸣声数据以WAV格式存储，所以声音数据未经压缩，无畸变。
首先，用小波降噪和自适应滤波的方法分别对含噪的果蝇飞行翅振鸣声信号进行消噪。结果表明：小波降噪对高频部分噪声的抑制十分有效，对噪声的频谱分析发现，噪声的能量主要集中在50Hz左右的低频处，用小波方法对低频段的降噪并不理想；采用自适应滤波方法可以将低频处的噪声有效地消除，因此自适应滤波在本文研究中是一种更为有效的去噪方法。
其次，从时域、频域分别对3个品系的雌、雄果蝇鸣声进行了分析。通过时域分析发现，3个品系果蝇飞行翅振鸣声的波形都为相似的正弦波，脉冲间距(IPI)略有不同。通过频域分析发现，同品系雌、雄果蝇飞行的翅振鸣声频率有很大重叠，平均雄性略高于雌性。而不同品系果蝇之间的飞行翅振鸣声频率也有重叠，略有差异。3个品系雌、雄果蝇飞行的翅振频率主频在200—300Hz之间，频率范围在0—4000Hz之间，能量主要集中在200—2000Hz，其中22号果蝇能量范围较大，主要集中在200—3000Hz。翅振鸣声所含频率成分都比较多，呈现为多谐谱。
最后，利用人工神经网络对3个品系果蝇进行分类识别。实验分为两种方案，每种方案实验分4组。第一种方案对每个鸣声样本进行频谱分析，取7个幅值最大的频率作为特征值。在这种方案中，神经网络设置为2个隐层。通过实验结果发现：神经网络对同品系内果蝇雌、雄的分类和种内不同品系雌性果蝇的分类效果都不理想。第二种方案对每个鸣声样本进行频谱分析，将129个频率点对应的功率谱密度作为输入特征向量。将每个品系雌、雄果蝇鸣声信号都分为训练样本集和验证样本集。每个训练样本集都包含50个鸣声信号样本，用训练样本集对所设计的人工神经网络进行训练。训练成功的网络，用对应的未参与训练的30个验证样本来验证神经网络。对同品系雌、雄果蝇进行识别，实验结果表明，神经网络对Canton-S果蝇雌性的识别率很高，对18号果蝇雄性的识别率很高，对22号果蝇雌性识别率很高。对另一性的识别率较低。可见同品系内雌、雄果蝇的翅振鸣声模式基本相同，翅振频率和声强特征相似，无法通过鸣声对同品系内的雌、雄果蝇进行分类识别。对3个不同品系(Canton-S、18号和22号)雌性果蝇进行识别，实验结果表明，Canton-S果蝇的平均识别率可达96.7％，18号果蝇的识别率可达100％，22号的识别率可达100％。可见，所建立的神经网络对种内不同品系间的果蝇飞行翅振鸣声具有较好的识别效果，利用鸣声信号特征的种内分类是可行和有效的，具有很好的推广性。飞行翅振鸣声的频域特征在果蝇种下分类中具有重要的识别意义。
Insects are closely related to human beings. They have direct or indirect influence on people's life. Insects communicate through particular sounds produced by various actions. The sounds produced by different actions mean differently. Studying the insect sound, analyzing its causes and features, and making comparison between different groups are of great significance to learn the rules of insects' actions and monitor and control the pests. The classification by sounds is helpful to classify the related species, dubious species and subspecies.
This paper has collected and analyzed the fruit fly's sound.
In the experiment, all the fruit flies are drosophila melanogasters provided by the School of Life Science of Shaanxi Normal University. There are three strains of them. One is Canton-Special(Canton-S) and the other two are mutant fruit flies numbered 18 and 22 respectively. Before the collection of sounds, each type is divided artificially into the male and the female. The wing-vibrating sounds of the male and the female are collected separately. The sounds are recorded and saved in WAV form without compression and aberration.
Firstly, the wing-vibrating sound signals of fruit flies, which have noise, are processed by means of wavelet denoising and adaptive filtering. What the results indicate are as follows. It is effective in controlling the noise in high frequency part using wavelet denoising. The energy of noise is in the low frequency part, around 50Hz in the spectrum. The noise in the low frequency is not eliminated effectively by wavelet denoising. Therefore, adaptive filtering proves to be an effective approach of removing noise in this research.
Secondly, the male and the female fruit flies of the three strains have been analyzed in terms of sounds from the time domain and frequency domain. With the analysis of time domain, the wing-vibrating sounds of the three strains have similar sine wave with slight difference in the interpulse interval (IPI). With the analysis of frequency domain, the male and the female of the same strain have a large overlapping part in the frequency of wing-vibrating sounds. On average, the frequency of the male is a bit higher than that of the female. Different strains also have overlaps in the frequency of wing-vibrating sounds, and little difference. The main frequency of the three strains ranges from 200 to 300Hz, frequency from 0 to 4000Hz, energy from 200 to 2000Hz. Number 22 has the largest range of energy from 200 to 3000Hz. There are many frequency components. It presents a multi-harmonic spectrum.
Thirdly, the classification of the three strains of fruit fly is conducted using neural network. The experiment is carried out in two ways, each of which is divided into four groups. Firstly, the spectrum analysis of each sound sample is made to take seven frequencies with the largest amplitude as the eigenvalue. The neural network is set in two implicit strata. As the result indicates, the neural network doesn't work effectively in classifying the male and the female of same strain and classifying the strains of same species. Secondly, the spectrum analysis of each sound sample is made to take 129 points in power spectral density estimate as the feature vector. Sound signals of the male and the female of each strain are divided into training collection and validation collection. Each training collection contains 50 signals which are used to train the artificial neural network. After the network is successfully trained, the neural network will be validated by the 30 corresponding validation samples of each strain of fruit fly. Neural network has a high level of identification accuracy for female Canton-S as well as for male number 18 and female number 22; while it has a level of identification accuracy for the other sex. It is clear that the male and the female of same strain have basically the same pattern of wing-vibrating sound, wing-vibrating frequency and sound intensity as well. The sex classification of same strain can not be conducted by sound. When the female fruit flies of Canton-S, number 18 and number 22 are analyzed, the average accuracy of identification of Canton-S reaches 96.7%, no.18 is 100%, and no.22 is 100%. As the result of the experiment demonstrates, the established neural network is effective in identifying the wing-vibrating sounds of different strains of same species. The utilization of sound signal features is applicable and feasible with great significance being popularized suitably. The features of frequency domain of wing-vibrating sounds have important significance to identification.
本文对同种不同品系果蝇飞行时翅振产生的鸣声进行了采集、分析研究。
实验中所用的果蝇均为黑腹果蝇，来源于陕西师范大学生命科学学院。共有同种下的3个品系，分别为Canton-Special(Canton-S)品系和标号为18号、22号的突变型果蝇。采集声音前，先将3个品系果蝇的雌、雄人工分离，然后分别对雌、雄果蝇飞行翅振鸣声进行采集。采集的鸣声数据以WAV格式存储，所以声音数据未经压缩，无畸变。
首先，用小波降噪和自适应滤波的方法分别对含噪的果蝇飞行翅振鸣声信号进行消噪。结果表明：小波降噪对高频部分噪声的抑制十分有效，对噪声的频谱分析发现，噪声的能量主要集中在50Hz左右的低频处，用小波方法对低频段的降噪并不理想；采用自适应滤波方法可以将低频处的噪声有效地消除，因此自适应滤波在本文研究中是一种更为有效的去噪方法。
其次，从时域、频域分别对3个品系的雌、雄果蝇鸣声进行了分析。通过时域分析发现，3个品系果蝇飞行翅振鸣声的波形都为相似的正弦波，脉冲间距(IPI)略有不同。通过频域分析发现，同品系雌、雄果蝇飞行的翅振鸣声频率有很大重叠，平均雄性略高于雌性。而不同品系果蝇之间的飞行翅振鸣声频率也有重叠，略有差异。3个品系雌、雄果蝇飞行的翅振频率主频在200—300Hz之间，频率范围在0—4000Hz之间，能量主要集中在200—2000Hz，其中22号果蝇能量范围较大，主要集中在200—3000Hz。翅振鸣声所含频率成分都比较多，呈现为多谐谱。
最后，利用人工神经网络对3个品系果蝇进行分类识别。实验分为两种方案，每种方案实验分4组。第一种方案对每个鸣声样本进行频谱分析，取7个幅值最大的频率作为特征值。在这种方案中，神经网络设置为2个隐层。通过实验结果发现：神经网络对同品系内果蝇雌、雄的分类和种内不同品系雌性果蝇的分类效果都不理想。第二种方案对每个鸣声样本进行频谱分析，将129个频率点对应的功率谱密度作为输入特征向量。将每个品系雌、雄果蝇鸣声信号都分为训练样本集和验证样本集。每个训练样本集都包含50个鸣声信号样本，用训练样本集对所设计的人工神经网络进行训练。训练成功的网络，用对应的未参与训练的30个验证样本来验证神经网络。对同品系雌、雄果蝇进行识别，实验结果表明，神经网络对Canton-S果蝇雌性的识别率很高，对18号果蝇雄性的识别率很高，对22号果蝇雌性识别率很高。对另一性的识别率较低。可见同品系内雌、雄果蝇的翅振鸣声模式基本相同，翅振频率和声强特征相似，无法通过鸣声对同品系内的雌、雄果蝇进行分类识别。对3个不同品系(Canton-S、18号和22号)雌性果蝇进行识别，实验结果表明，Canton-S果蝇的平均识别率可达96.7％，18号果蝇的识别率可达100％，22号的识别率可达100％。可见，所建立的神经网络对种内不同品系间的果蝇飞行翅振鸣声具有较好的识别效果，利用鸣声信号特征的种内分类是可行和有效的，具有很好的推广性。飞行翅振鸣声的频域特征在果蝇种下分类中具有重要的识别意义。
Insects are closely related to human beings. They have direct or indirect influence on people's life. Insects communicate through particular sounds produced by various actions. The sounds produced by different actions mean differently. Studying the insect sound, analyzing its causes and features, and making comparison between different groups are of great significance to learn the rules of insects' actions and monitor and control the pests. The classification by sounds is helpful to classify the related species, dubious species and subspecies.
This paper has collected and analyzed the fruit fly's sound.
In the experiment, all the fruit flies are drosophila melanogasters provided by the School of Life Science of Shaanxi Normal University. There are three strains of them. One is Canton-Special(Canton-S) and the other two are mutant fruit flies numbered 18 and 22 respectively. Before the collection of sounds, each type is divided artificially into the male and the female. The wing-vibrating sounds of the male and the female are collected separately. The sounds are recorded and saved in WAV form without compression and aberration.
Firstly, the wing-vibrating sound signals of fruit flies, which have noise, are processed by means of wavelet denoising and adaptive filtering. What the results indicate are as follows. It is effective in controlling the noise in high frequency part using wavelet denoising. The energy of noise is in the low frequency part, around 50Hz in the spectrum. The noise in the low frequency is not eliminated effectively by wavelet denoising. Therefore, adaptive filtering proves to be an effective approach of removing noise in this research.
Secondly, the male and the female fruit flies of the three strains have been analyzed in terms of sounds from the time domain and frequency domain. With the analysis of time domain, the wing-vibrating sounds of the three strains have similar sine wave with slight difference in the interpulse interval (IPI). With the analysis of frequency domain, the male and the female of the same strain have a large overlapping part in the frequency of wing-vibrating sounds. On average, the frequency of the male is a bit higher than that of the female. Different strains also have overlaps in the frequency of wing-vibrating sounds, and little difference. The main frequency of the three strains ranges from 200 to 300Hz, frequency from 0 to 4000Hz, energy from 200 to 2000Hz. Number 22 has the largest range of energy from 200 to 3000Hz. There are many frequency components. It presents a multi-harmonic spectrum.
Thirdly, the classification of the three strains of fruit fly is conducted using neural network. The experiment is carried out in two ways, each of which is divided into four groups. Firstly, the spectrum analysis of each sound sample is made to take seven frequencies with the largest amplitude as the eigenvalue. The neural network is set in two implicit strata. As the result indicates, the neural network doesn't work effectively in classifying the male and the female of same strain and classifying the strains of same species. Secondly, the spectrum analysis of each sound sample is made to take 129 points in power spectral density estimate as the feature vector. Sound signals of the male and the female of each strain are divided into training collection and validation collection. Each training collection contains 50 signals which are used to train the artificial neural network. After the network is successfully trained, the neural network will be validated by the 30 corresponding validation samples of each strain of fruit fly. Neural network has a high level of identification accuracy for female Canton-S as well as for male number 18 and female number 22; while it has a level of identification accuracy for the other sex. It is clear that the male and the female of same strain have basically the same pattern of wing-vibrating sound, wing-vibrating frequency and sound intensity as well. The sex classification of same strain can not be conducted by sound. When the female fruit flies of Canton-S, number 18 and number 22 are analyzed, the average accuracy of identification of Canton-S reaches 96.7%, no.18 is 100%, and no.22 is 100%. As the result of the experiment demonstrates, the established neural network is effective in identifying the wing-vibrating sounds of different strains of same species. The utilization of sound signal features is applicable and feasible with great significance being popularized suitably. The features of frequency domain of wing-vibrating sounds have important significance to identification.
引文
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