Automatic Classification of Bird Sounds: Using MFCC and Mel Spectrogram Features with Deep Learning

Abstract

Bird species identification is a relevant and time-consuming task for ornithologists and ecologists. With growing amounts of audio-annotated data, automatic bird classification using machine learning techniques is an important trend in the scientific community. Analyzing bird behavior and population trends helps detect other organisms in the environment and is an important problem in ecology. Bird populations react quickly to environmental changes, which make their real-time counting and tracking challenging and very useful. A reliable methodology that automatically identifies bird species from audio would therefore be a valuable tool for the experts in different scientific and applicational domains. The goal of this work is to propose a methodology to identify bird sounds. In this paper, we explore deep learning techniques that are being used in this domain, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to classify the data. In deep learning, audio problems are commonly approached by converting them into images using audio feature extraction techniques such as Mel Spectrograms and Mel Frequency Cepstral Coefficients (MFCCs). We propose and test multiple deep learning and feature extraction combinations in order to find the most suitable approach to this problem.