Deep Audio Classification and Recognization with Oscillations and Wave Formations

Abstract

This study proposes an automated system for identifying Capuchin bird calls from audio recordings in a forest environment. The system uses convolutional neural networks (CNNs) to classify short segments of audio data represented as spectograms. The Automatic identification and spatial analysis of capuchin bird calls in a forest ecosystem. We are using the power of deep learning to develop a system to efficiently process large audio recordings, which will provide researchers and conservationists with valuable information about the distribution of capuchin birds. The core of the system lies in a convolutional neural network (CNN) trained to classify short audio segments, represented as spectograms. We achieve this through a multi-step process. First, raw audio data recorded in a forest environment is converted into signals to visually represent sound pressure fluctuations. These waveforms are later converted into spectograms, providing a visual representation of the frequencies present in the audio data over time. This spectogram serves as an ideal input signal for CNN due to its similarity to images. This system lies in its ability to analyze audio data from various locations in the forest. By comparing the number of capuchin calls identified in records from different regions, we can create spatial distribution maps that highlight areas with the highest densities of capuchins. This information has proven invaluable to researchers studying capuchin behavior, habitat preferences, and population dynamics. It could also be an important tool in conservation efforts to target stocks and protect areas with high capuchin densities.