基于Mel子带参数化特征的自动鸟鸣识别  

Automatic bird vocalization identification based on Mel-subband parameterized feature

作　　者：张赛花[1] 赵兆[1] 许志勇[1] 张怡[1] 

机构地区：[1]南京理工大学电子工程与光电技术学院,南京210094

出　　处：《计算机应用》2017年第4期1111-1115,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61401203;61171167);江苏省自然科学基金资助项目(BK20130776)~~

摘　　要：针对自然复杂声学环境下基于鸟鸣的物种分类问题,提出了一种基于Mel子带参数化特征的鸟鸣自动识别方法。采用高斯混合模型(GMM)拟合连续声学监测数据分帧后的对数能量分布,选取高似然率的数据帧组成候选声音事件完成自动分段。在谱图域对相应片段采用Mel带通滤波器组滤波处理,然后基于自回归模型(AR)分别建模各个子带输出的随时间变化的能量序列,得到能够描述不同种类鸟鸣信号时频特性的参数化特征。最后利用支持向量机(SVM)分类器进行分类识别。基于野外自然环境11种鸟鸣信号开展了自动分段与识别实验,所提方法针对各类鸟鸣的查准率、查全率以及F1度量均不低于89%,明显优于现有基于纹理特征的方法,更适用于野外鸟类连续声学监测领域的自动数据分析需求。Aiming at the vocalization-based bird species classification in natural acoustic environments, an automatic bird vocalization identification method was proposed based on a new Mel-subband parameterized feature. The field recordings were first divided into consecutive frames and the distribution of log-energies of those frames were estimated using Gaussian Mixture Model（GMM） of two mixtures. The frames with respect to high likelihood were selected to compose initial candidate acoustic events. Afterwards, a Mel band-pass filter-bank was first employed on the spectrogram of each event. Then, the output of each subband, i. e. a time-series containing time-varying band-limited energy, was parameterized by an AutoRegressive（AR）model, which resulted in a parameterized feature set consisting of all model coefficients for each bird acoustic event. Finally,the Support Vector Machine（SVM） classifier was utilized to identify bird vocalization. The experimental results on real-field recordings containing vocalizations of eleven bird species demonstrate that the precision, recall and F1-measure of the proposed method are all not less than 89%, which indicates that the proposed method considerably outperforms the state-of-the-art texture-feature-based method and is more suitable for automatic data analysis in continuous monitoring of songbirds in natural environments.

关 键 词：鸟鸣 自动识别 Mel子带 时间序列建模 支持向量机 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]