【摘要】 【目的】从原始鸟类音频频谱图样本集中自动筛选并剔除噪音频谱图,可以提升鸟类物种分类的准确率。【方法】本文基于卷积神经网络,对频谱图提取特征向量,借助Faiss算法库计算特征向量的距离矩阵,然后使用 DBSCAN (Density-Based Spatial Clustering of Applications with Noise) 聚类算法筛选出噪音频谱图,最后将经过筛选后的频谱图样本集输入到分类模型中进行鸟类物种分类。【结果】通过本方法,从频谱图样本集中剔除了大量噪音频谱图,使得后续的鸟类物种的分类准确率得到了提升。【局限】由于 DBSCAN 算法聚类的效果受到邻域阈值 (ε) 和密度阈值 (MinPts) 参数的影响比较大,因此未来应该去探索自适应的方法获得参数值。【结论】本文将卷积神经网络和数据挖掘中的密度聚类算法相结合,提出了一种鸟类音频数据预处理方法,该方法可以自动筛选噪音频谱图,为后续的鸟类物种识别提供了高质量的频谱图样本集。更多还原

【Abstract】 [Objective]The accuracy of bird species classification can be improved by noise spectrogram filtering and removing from the sample set of original bird audio spectrograms.[Methods]Based on the convolutional neural network,this paper extracts the feature vector from the spectrogram,calculates the distance matrix of the feature vector with the Faiss algorithm library,and then uses the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm to filter out the noise spectrogram.Finally,the filtered spectrogram sample set is input into the classification model for bird species classification.[Results]Through this method,a large number of noise spectrograms are removed from the spectrogram sample set so that the accuracy of subsequent bird species classification has been improved.[Limitations]Because the clustering effect of the DBSCAN algorithm is greatly affected by the neighborhood threshold (ε) and density threshold (MinPts) parameters,we should explore adaptive methods to obtain parameter values in the future.[Conclusions]This paper combines the convolutional neural network and the density clustering algorithm in data mining and proposes a bird audio data preprocessing method for automatically noise spectrogram filtering,which provides a high-quality spectrogram sample set for subsequent bird species identification.更多还原