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Identification of bird species from audio records is one of the challengingtasks due to the existence of multiple species in the same recording, noise inthe background, and long-term recording. Besides, choosing a proper acousticfeature from audio recording for bird species classification is anotherproblem. In this paper, a hybrid method is represented comprising bothtraditional signal processing and a deep learning-based approach to classifybird species from audio recordings of diverse sources and types. Besides, adetailed study with 34 different features helps to select the proper featureset for classification and analysis in real-time applications. Moreover, theproposed deep neural network uses both acoustic and temporal feature learning.The proposed method starts with detecting voice activity from the raw signal,followed by extracting short-term features from the processed recording using50 ms (with 25ms overlapping) time windows. Later, the short-term features arereshaped using second stage (non-overlapping) windowing to be trained through adistributed 2D Convolutional Neural Network (CNN) that forwards the outputfeatures to a Long and Short Term Memory (LSTM) Network. Then a final denselayer classifies the bird species. For the 10 class classifier, the highestaccuracy achieved was 90.45\% for a feature set consisting of 13 Mel FrequencyCepstral Coefficients (MFCCs) and 12 Chroma Vectors. The correspondingspecificity and AUC scores are 98.94\% and 94.09\%, respectively.

Bird Species Classification And Acoustic Features Selection Based on Distributed Neural Network with Two Stage Windowing of Short-Term Features