Automatic phrase detection systems of bird sounds are useful in several applications as they reduce the need for manual annotations. However, birdphrase detection is challenging due to limited training data and background noise. Limited data occur because of limited recordings or the existence of rare phrases. Background noise interference occurs because of the intrinsic nature of the recording environment such as wind or other animals. This paper presents a different approach to birdsong phrase classification using template-based techniques suitable even for limited training data and noisy environments. The algorithm utilizes dynamic time-warping (DTW) and prominent (high-energy) time-frequency regions of training spectrograms to derive templates. The performance of the proposed algorithm is compared with the traditional DTW and hidden Markov models (HMMs) methods under several training and test conditions. DTW works well when the data are limited, while HMMs do better when more data are available, yet they both suffer when the background noise is severe. The proposed algorithm outperforms DTW and HMMs in most training and testing conditions, usually with a high margin when the background noise level is high. The innovation of this work is that the proposed algorithm is robust to both limited training data and background noise.