Developing a Model for Bird Vocalization Recognition and Population Estimation in Forest Ecosystems

Bird audio recognition, particularly identifying specific species based on their vocalizations, holds significant potential in various fields. From environmental studies to wildlife monitoring and even conservation efforts, accurate identification of bird species can provide critical insights into biodiversity, population trends, and behaviour patterns. However, traditional methods of bird identification often rely heavily on field guides and human observers, which can be time-consuming, subjective, and prone to errors. This study introduces a novel model designed to identify the Capuchin bird voice among others using machine learning techniques. The model leverages the power of Convolutional Neural Networks (CNNs) to analyze spectrograms. This approach allows to make the process of identification of bird voices much faster and more accurate. The model's ability to count birds chirping can contribute significantly to our understanding of avian biodiversity and behaviour. It can aid in the early detection of rare or endangered species, monitor changes in bird populations over time, and even inform strategies for habitat conservation. Furthermore, this technology could also be integrated into smartphone apps or IoT devices, enabling everyday citizens to contribute to bird surveillance and conservation efforts. While this study focuses on the Capuchin bird voice, the model's architecture and training process could be adapted to recognize other bird species as well, expanding its utility and applicability. In conclusion, the development of this bird audio recognition model represents a significant step forward in harnessing the power of machine learning for environmental research and conservation.

I. Introduction

Avian vocalization studies are essential for ecological research, conservation planning, species distribution modelling, and mitigating risks like bird-plane collisions. Traditionally, these studies required labor-intensive manual surveys by trained observers. However, the advent of machine learning and digital signal processing technologies has shifted the focus towards automated bird population surveys, offering a more efficient and cost-effective approach. Bird Audio Detection (BAD), a key aspect of these automated surveys, aims to identify bird calls within audio recordings. Despite its potential, BAD faces challenges due to the use of weakly labelled data (WLD) and the complexity of forest soundscapes. This paper proposes a novel approach using Convolutional Neural Networks (CNNs) for acoustic bird audio classification. The method involves creating spectrograms from the audio data for the CNN model, which is trained to recognize specific bird species' vocal features within forest recordings. The model's results are compiled into a CSV file, quantifying the bird's vocal presence, and contributing significantly to ecological research. The paper will further discuss previous methods used in the audio field, delve into the methodology, and suggest future research directions. The traditional approach to collecting bird species data has involved manual surveys, which are labour-intensive and require observers trained in bird recognition. With the advent of machine learning and digital signal processing technologies, there has been a significant shift towards automated bird population surveys, which have the potential to provide vast amounts of valuable data with less effort and expense than human surveys.