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.
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