Vocalization Patterns in Laying Hens - An Analysis of Stress-Induced Audio Responses

This repository houses a comprehensive collection of data and resources from the study "Vocalization Patterns in Laying Hens - An Analysis of Stress-Induced Audio Responses." Led by Dr. Suresh Neethirajan at Mooanalytica, Department of Agriculture & Aquaculture, Faculty of Agriculture & Computer Science, Dalhousie University, this research represents a significant foray into the field of poultry ethology and welfare monitoring using advanced machine learning techniques.

Key Components of the Repository

1. Experimental Audio Data

   • Control and Treatment Vocalizations: Audio recordings of laying hens under two different stress conditions – sudden umbrella opening (Treatment 1) and simulated dog barking sounds (Treatment 2), along with control groups. The processed dataset is approximately 460 MB for the control group experimental data and about 2 GB for the 2 treatment group experimental data, capturing the nuanced responses of hens to these stressors.
   • Original Raw Data: The original, unprocessed audio data is around 9 GB in size. Though not included in the repository, it can be made available upon reasonable request.

2. Algorithm and Code Files

   • CNN Feature Extraction and Classification Algorithms Python scripts used for the extraction of features from the audio data using Convolutional Neural Networks (CNN) and subsequent classification.
   • Supplementary Algorithms Additional code files that support the processing and analysis of the audio data.

3. MFCC Feature Dataset

   • An Excel file containing the 40 Mel Frequency Cepstral Coefficients (MFCC) features extracted from the vocalization data. This dataset provides a detailed spectral analysis of the hen's vocalizations, crucial for understanding their response to stress.

Study Overview

This study aimed to classify and analyze the vocalization patterns of laying hens subjected to different stressors. Using a CNN model, the research identified distinct vocal patterns between control and treated groups, indicating unique vocal responses to different types of stressors. This study is pivotal in understanding the impact of environmental stressors on poultry welfare and behavior. The age of the chickens and the timing of stressor application were also critical factors influencing vocalization patterns.

Implications and Applications:

The findings from this study have significant implications for poultry welfare monitoring and management. By providing a non-invasive method to assess the well-being of chickens, this research contributes valuable insights into enhancing poultry management practices and welfare standards.

The resources in this repository are intended for researchers, academicians, and professionals in animal behavior, veterinary science, and poultry management. We encourage the use of these data and tools for further research and practical applications in the field of precision (Digital) livestock farming and animal welfare.

For any queries or requests related to the raw dataset, please contact Dr. Suresh Neethirajan.