Identifying bird species by their calls in Soundscapes

Abstract

In many real data science problems, it is common to encounter a domain mismatch between the training and testing datasets, which means that solutions designed for one may not transfer well to the other due to their differences. An example of such was in the BirdCLEF2021 Kaggle competition, where participants had to identify all bird species that could be heard in audio recordings. Thus, multi-label classifiers, capable of coping with domain mismatch, were required. In addition, classifiers needed to be resilient to a long-tailed (imbalanced) class distribution and weak labels. Throughout the competition, a diverse range of solutions based on convolutional neural networks were proposed. However, it is unclear how different solution components contribute to overall performance. In this work, we contextualise the problem with respect to the previously existing literature, analysing and discussing the choices made by the different participants. We also propose a modular solution architecture to empirically quantify the effects of different architectures. The results of this study provide insights into which components worked well for this challenge.

Bird Call Identification

“…As many competitions were launched for identifying bird voices, hence, many approaches were proposed for classi cation. The most common approach is using CNN [1] [2]. Some of the approaches use machine learning [3], some use neural networks [4] while some combine machine learning and neural networks like combination of ANN(Arti cial Neural Network) and SVM [5] [6], combination of ANN and KNC(K-Nearest Centroid) where SVM and KNC are used for processing audio signals before feeding them into network.…”