Bird species recognition method based on Chirplet spectrogram feature and deep learning

ObjectiveThe application of deep learning in bird species recognition is the research hotspot at present. To improve the performance of recognition, a bird species recognition method based on Chirplet spectrogram feature and VGG16 model was proposed.

MethodAcoustic signal spectrograms were calculated by the Chirplet transform firstly, then spectrograms were inputted in the VGG16 model to realize the recognition of bird species. Taking eighteen bird species in Beijing Songshan National Nature Reserve as examples, through Chirplet transform, Fourier transform and Mel cepstrum transform, three spectrogram sample sets were calculated respectively, then using three kinds of spectrogram sample sets to train the recognition model, the performances of each input were compared.

ResultResults showed that with the Chirplet diagram input, the highest mean average precision (MAP) of the test set was 0.9871 compared with the other two inputs. Also, the epochs of the highest trainning MAP was the smallest.

ConclusionThe choice of input affects the classification performance of deep learning model. The vocalization zone of Chirplet spectrogram is more concentrate and obvious than STFT spectrogram and Mel spectrogram, which means Chirplet spectrogram is more suitable for the bird recognition based on VGG16 model, higher MAP and efficiency of recognition can be achieved.