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.